Identification Algorithm Research Articles

Integrated Structural Hole and K-shell Algorithm for Tsallis Entropy-based Identification of Key Nodes in Power Grids

Abstract Considering the low-carbon development goals of “peak carbon emissions and carbon neutrality,” traditional energy enterprises, including oil fields, have accelerated the incorporation of new energy into their power grids. However, incorporating new energy generation into traditional oilfield power grids yields a series of safety hazards, making the stability of the oilfield power grid structure increasingly important. In this paper, a redefined theory of structural holes and the K-shell algorithm are utilized to identify both local and global key nodes in the oilfield power grid. The improved Tsallis entropy is employed to recognize these key nodes, accounting for their local influence within the oilfield power grid as well as their global status. Additionally, considering the electrical characteristics of the nodes, a set of measurement metrics suitable for oilfield power grid research is constructed. Finally, the IEEE-39 feeder system is simulated and compared with other key node identification methods. By analyzing the robustness of the topological structure and the loss load value of the power system after removing key nodes, the reliability and superiority of the proposed method are verified.

Machine learning-based grayscale analyses for lithofacies identification of the Shahejie Formation, Bohai Bay Basin, China

It is of great significance to accurately and rapidly identify shale lithofacies in relation to the evaluation and prediction of sweet spots for shale oil and gas reservoirs. To address the problem of low resolution in logging curves, this study establishes a grayscale-phase model based on high-resolution grayscale curves using clustering analysis algorithms for shale lithofacies identification, working with the Shahejie Formation, Bohai Bay Basin, China. The grayscale phase is defined as the sum of absolute grayscale and relative amplitude as well as their features. The absolute grayscale is the absolute magnitude of the gray values and is utilized for evaluating the material composition (mineral composition + total organic carbon) of shale, while the relative amplitude is the difference between adjacent gray values and is used to identify the shale structure type. The research results show that the grayscale phase model can identify shale lithofacies well, and the accuracy and applicability of this model were verified by the fitting relationship between absolute grayscale and shale mineral composition, as well as corresponding relationships between relative amplitudes and laminae development in shales. Four lithofacies are identified in the target layer of the study area: massive mixed shale, laminated mixed shale, massive calcareous shale and laminated calcareous shale. This method can not only effectively characterize the material composition of shale, but also numerically characterize the development degree of shale laminae, and solve the problem that difficult to identify millimeter-scale laminae based on logging curves, which can provide technical support for shale lithofacies identification, sweet spot evaluation and prediction of complex continental lacustrine basins.

Mission-oriented capability evaluation for combat network based on operation loops

With continuous growth in scale, topology complexity, mission phases, and mission diversity, challenges have been placed for efficient capability evaluation of modern combat systems. Aiming at the problems of insufficient mission consideration and single evaluation dimension in the existing evaluation approaches, this study proposes a mission-oriented capability evaluation method for combat systems based on operation loop. Firstly, a combat network model is given that takes into account the capability properties of combat nodes. Then, based on the transition matrix between combat nodes, an efficient algorithm for operation loop identification is proposed based on the Breadth-First Search. Given the mission-capability satisfaction of nodes, the effectiveness evaluation indexes for operation loops and combat network are proposed, followed by node importance measure. Through a case study of the combat scenario involving space-based support against surface ships under different strategies, the effectiveness of the proposed method is verified. The results indicated that the ROI-priority attack method has a notable impact on reducing the overall efficiency of the network, whereas the O-L betweenness-priority attack is more effective in obstructing the successful execution of enemy attack missions.

Identification of PV Parameters Based on Bézier Curve and AWSO

AbstractTo achieve accurate PV cell parameter identification, a method combining the second‐order Bézier function with the adaptive war strategy optimization (AWSO) algorithm is proposed, the Bézier curve for fitting the PV output characteristic curve is given by using the linear connection between the filling factor and the control points of the second‐order Bézier function, and then, the Bézier‐AWSO model is constructed, and an adaptive weight‐based updating and allocation method is given to improve the global search capability of the AWSO algorithm in the problem of recognizing five important parameters of photovoltaic, and avoid falling into the local optimum in the process of recognition. Finally, the optimal solution for the unknown parameters in the single diode topology of silicon‐based photovoltaic cells is obtained by using the data points of the fitted curves and the AWSO algorithm, and the accuracy of the AWSO algorithm for photovoltaic cell parameter identification is verified through the analysis of the arithmetic examples and real experiments. The results demonstrate that the parameter identification of the proposed improved model reduces the average relative error by 0.5%–1% compared with that before the improvement under standard and non‐standard test conditions, which improves the accuracy of the 5‐parameter identification results of the silicon‐based PV cell and provides a reference for the subsequent fault analysis. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Study of Experimental and Assembly Uncertainties on Modal Parameter Estimates of Axial Piston Pumps

Finite Element Model Updating (FEMU) plays a pivotal role in enhancing the accuracy and reliability of structural dynamics models. Nonetheless, uncertainties related to experimental vibration testing and natural manufacturing variability introduce unavoidable variability in the results of Modal Parameter Extraction (MPE), which is the basis for updating. In this regard, this study delves into the inherent variability of estimates from MPE following from experimental vibration testing Axial Piston Pumps (APPs). Accordingly, we investigate the effect of material properties, boundary conditions and measurement noise, through several experimental vibration tests involving assembly-disassembly and exchange of components. These variations account for inherent manufacturing variability and tolerances both at the component and material level. The obtained vibration data is processed using non-parametric FRF estimates and the Stochastic Subspace Identification (SSI) algorithm, resulting in a collection of FRFs and modal parameter estimates (natural frequencies, damping ratios and mode shapes). We use multivariate analysis techniques to quantify the uncertainty of these estimates and to assess the individual effect of assembly and population uncertainties. These results serve as a basis for further investigation towards the updating of a FE model of the APP, and the quantification of modal parameter uncertainties within the material and substructure connectivity properties.

Calib-RT: an open source python package for peptide retention time calibration in DIA mass spectrometry data.

The data independent acquisition (DIA) mass spectrometry (MS) method is increasingly popular in the field of proteomics. But the loss of the correspondence between peptide ions and their spectra in DIA makes the identification challenging. One effective approach to reduce false positive identification is to calculate the deviation between the peptide's estimated retention time (RT) and measured RT. During this process, scaling the spectral library RT into the estimated RT, known as the RT calibration, is a prerequisite for calculating the deviation. Currently, within the DIA algorithm ecosystem, there is a lack of engine-independent and readily usable RT calibration toolkits. In this work, we introduce Calib-RT, a RT calibration method tailored to the characteristics of RT data. This method can achieve the nonlinear calibration across various data scales and tolerate a certain level of noise interference. Calib-RT is expected to enrich the open source DIA algorithm toolchain and assist in the development of DIA identification algorithms. Calib-RT is released as an open source software under the MIT license and can be installed from PyPi as a python module. The source code is available on GitHub at https://github.com/chenghui03/Calib_RT.

Frequency-dispersed Ion Acoustic Waves in the Near-Sun Solar Wind: Signatures of Impulsive Ion Beams

This work reports a novel plasma wave observation in the near-Sun solar wind: frequency-dispersed ion acoustic waves. Similar waves have previously been reported in association with interplanetary shocks or planetary bow shocks, but the waves reported here occur throughout the solar wind sunward of ∼60 solar radii, far from any identified shocks. The waves reported here vary their central frequency by factors of 3–10 over tens of milliseconds, with frequencies that move up or down in time. Using a semiautomated identification algorithm, thousands of wave instances are recorded during each near-Sun orbit of the Parker Solar Probe spacecraft. Wave statistical properties are determined and used to estimate their plasma frame frequency and the energies of protons most likely to be resonant with these waves. Proton velocity distribution functions are explored for one wave interval, and proton enhancements that may be consistent with proton beams are observed. A conclusion from this analysis is that properties of the observed frequency-dispersed ion acoustic waves are consistent with driving by cold, impulsively accelerated proton beams near the ambient proton thermal speed. Based on the large number of observed waves and their properties, it is likely that the impulsive proton beam acceleration mechanism generating these waves is active throughout the inner heliosphere. This may have implications for the acceleration of the solar wind.

DriverDetector: An R package providing multiple statistical methods for cancer driver genes detection and tools for downstream analysis

Identifying driver genes in cancer is a difficult task because of the heterogeneity of cancer as well as the complex interactions among genes. As sequencing data become more readily available, there is a growing need for detecting cancer driver genes based on statistical and mathematical modeling methods. Currently, plenty of driver gene identification algorithms have been published, but they fail to achieve consistent results. In order to obtain gene sets with high confidence, we present DriverDetector, an R package providing a convenient workflow for cancer driver genes detection and downstream analysis. We develop the background mutation rate calculating module based on the distance between genes in covariate space and binomial test, followed by the driver gene selection module which integrates 11 methods, including two already recognized approaches, a de novo method, and five variants of Fisher's method which are applied to driver gene identification for the first time. Through verification on 12 TCGA datasets, each method is able to identify a set of confirmed driver genes while the number of resulting genes vary significantly across different methods. For robust driver genes detection, a voting strategy based on 10 of the statistical methods is further applied. Results show that the collective prediction based on the voting strategy demonstrates superiority in achieving the consistency of prediction while ensuring a reasonable number of predicted genes and confirmed drivers. By comparing the results of each cancer dataset, we also find that sample size has a huge impact on the number of predicted genes. For downstream analysis, DriverDetector automatically generates plenty of plots and tables to elaborate the results. We propose DriverDetector as a user-friendly tool promoting early diagnosis of cancer and the development of targeted drugs.

Fast Bayesian modal identification with known seismic excitations

AbstractFast and accurate identification of structural modal parameters after an earthquake is crucial for assessing structural conditions and facilitating repair. With the development of modern earthquake observation techniques, the recorded ground motion can be leveraged as extra input information for modal identification, enabling the experimental modal analysis applicable. This study develops a Bayesian modal identification algorithm that aims at estimating the most probable value (MPV) of modal parameters and their identification uncertainty. Incorporating the recorded seismic input, the algorithm utilizes with the structural equation of motion in the frequency domain to formulate the likelihood function and adopts a constrained Laplace method for Bayesian posterior approximation of modal parameters. With the aid of complex matrix calculus, an iterative scheme is developed, allowing a fast search of the MPV of modal parameters and an analytical evaluation of the posterior covariance matrix. The performance of the proposed algorithm is validated by examples with synthetic, laboratory and field data, respectively. In addition, its effectiveness on predicting structural responses under a future earthquake is illustrated, showing its potential for various downstream applications in seismic structural health monitoring.

Welded joints stiffness and damping characterisation based on model updating and texture analysis

Welded structures are integral to complex bodies such as ships and offshore rigs. Welded joints are frequently used in connecting various plates and substructures, making their assessment pivotal. The numerical model used here consists of plates coupled using springs and dashpots. The coupling models the welded joint and exemplifies the uncertainties in the welding process. The joint identification algorithm systematically defines the coupling in two steps: first, by updating the model for spring stiffness and later, by identifying the dashpot coefficients. The dynamic characteristic is determined using a frequency response function (FRF). After model updating and damping identification, a comparison between the updated FRF and that obtained from the experimental modal test is carried out to ascertain the efficiency of the algorithm. Further, the test structure is subjected to experimental testing to correlate the results from the model updating with material properties. The experimental study includes tensile testing of the weld joints and microstructural analysis. The characterisation is carried out by partitioning the welded joint. The joint stiffness and damping are correlated to the stress–strain response and crystallographic texture of the welded plate.

Identification of Hammerstein models with hysteresis nonlinearity

This paper deals with the identification of Hammerstein nonlinear models. The system nonlinearity is not necessarily static and can be of hysteresis type. The latter nonlinearity can be backlash, switch or any other memory operator having several transient cycles, but one permanent (major) cycle. The LTI (Linear Time-Invariant) block is nonparametric and is described by its impulse response. The considered system’s nonlinearity is any memory operator having one major cycle (periodic closed loop), which is bordered by any two arbitrary functions. The nonlinearity borders are not assumed to be invertible. The proposed identification algorithm can be used in static nonlinearity, e.g. polynomial nonlinearity, dead zone function or any piecewise affine functions. Unlike several previous studies, the proposed identification method is carried out using only one stage. In this approach, an input signal of piecewise constant (staircase) is used. It is shown that the estimated parameters converge to their true values.

Parameter Identification of Maritime Vessel Rudder PMSM Based on Extended Kalman Particle Filter Algorithm

To address the issue of system parameter variations during the operation of a maritime light vessel rudder permanent magnet synchronous motor (PMSM), an extended Kalman particle filter (EKPF) algorithm that combines a particle filter (PF) with an extended Kalman filter (EKF) is proposed in this paper. This approach enables the online identification of motor resistance and inductance. For highly nonlinear problems that are challenging for traditional methods such as Kalman filtering, this algorithm is typically a statistical and effective estimation method that usually yields good results. Firstly, a standard linear discrete parameter identification model is established for a PMSM. Secondly, the PF algorithm based on Bayesian state estimation as a foundation for subsequent research is derived. Thirdly, the advantages and limitations of the PF algorithm are analyzed, addressing issues such as sample degeneracy, by integrating it with the Kalman filtering algorithm. Specifically, the EKPF algorithm for online parameter identification is employed. Finally, the identification model within MATLAB/Simulink is constructed and the simulation studies are executed to ascertain the viability of our suggested algorithm. The outcomes from these simulations indicate that the proposed EKPF algorithm identifies resistance and inductance values both swiftly and precisely, markedly boosting the robustness and enhancing the control efficacy of the PMSM.

Application of Object Detection Algorithm for Efficient Damages Identification of the Conservation of Heritage Buildings

ABSTRACTHeritage buildings are crucial for any area's cultural and political aspects. Proper maintenance and monitoring are essential for the conservation of these buildings. However, manual inspections are time‐consuming and expensive. We propose a deep learning–based detection framework to identify the damages on the ancient architectural wall. The algorithm applied in this study is YOLOv5. Comparing its five different versions, it was decided to use YOLOv5m as the most accurate detection algorithm with a mAP of 0.801. The damage types identified are physical weathering and visitors' scratches. High‐resolution images were selected for the experiment and effectively identified image. In addition, the applied algorithm allows real‐time detection and the identification of seasonal sources of disruption, which is proved by the video test in this study. The findings contribute to the development of an intelligent tool for health monitoring with the goal of fast and remote damage detection in the routine maintenance of heritage buildings.

An In-Run Automatic Demodulation Phase Error Compensation Method for MEMS Gyroscope in Full Temperature Range.

The demodulation phase error will cause the quadrature error to be coupled to the rate output, resulting in performance deterioration of the MEMS gyroscope. To solve this problem, an in-run automatic demodulation phase error compensation method is proposed in this paper. This method applies square wave angular rate input to the gyroscope and automatically identifies the value of the demodulation phase error through the designed automatic identification algorithm. To realize in-run automatic compensation, the demodulation phase error corresponding to the temperature point is measured every 10 °C in the full-temperature environment (-40~60 °C). The relationship between temperature and demodulation phase error is fitted by a third-order polynomial. The temperature is obtained by the temperature sensor and encapsulated in the ceramic packages of the MEMS gyroscope, and the in-run automatic compensation is realized based on the fitting curve. The temperature hysteresis effect on the zero-rate output (ZRO) of the gyroscope is eliminated after compensation. The bias instability (BI) of the three gyroscopes at room temperature (25 °C) is reduced by four to eight times to 0.1°/h, while that at full-temperature environment (-40~60 °C) is reduced by three to four times to 0.1°/h after in-run compensation.

Nonlinear RF impairments modeling with OFDM symbol-based parallel Wiener–Hammerstein structure

In the paper, a novel orthogonal frequency division multiplexing (OFDM) symbol-based parallel Wiener–Hammerstein structure is proposed to deal with the problem of nonlinear radio frequency (RF) impairments modeling for a cyclic prefix OFDM (CP-OFDM) waveform in telecommunication systems. The aforementioned structure is an equivalent linear-in-parameters representation of the parallel Wiener–Hammerstein structure in the considered problem. The structure is formally derived enabling the usage of identification algorithms compared with the artificial neural network (ANN) approach. The solution is compliant with current telecommunication standards.

Parameter Optimization in Cluster Identification Algorithms for Characterizing Nanoclusters in Al-Mg-Si-Cu Alloys.

Optimization of user-defined parameters (Dmax, Nmin, order (K)) in the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, used to characterize nanoclusters in Al-0.9% Mg-1.0% Si-0.3% Cu (mass %), was conducted. Ten combinations of parameters with a given K were considered for samples naturally aged (NA) and preaged (PA) at 100°C. We confirmed four types of unphysical clusters, artificially formed, by analyzing composition with size, atomic density, and atomic arrangement inside clusters. The optimum combinations minimizing those unphysical clusters were obtained for both NA and PA samples. Meanwhile, to evaluate the reliability of the optimum combination, volume rendering and isosurfacing were performed. As a result, regions of high solute concentration were confirmed, and those regions are in good agreement with the position of the clusters obtained by applying the optimum combination in DBSCAN. Furthermore, by comparing the optimum combinations with the fixed parameters widely used until now, we showed that for each dataset, considering independent parameters obtained in the same method is desirable rather than using fixed parameters. Consequently, an idea of determining the algorithm parameters for characterizing the nanoclusters in Al-Mg-Si(-Cu) alloys was introduced.

Monitoring Biophysical Variables (FVC, LAI, LCab, and CWC) and Cropland Dynamics at Field Scale Using Sentinel-2 Time Series

Biophysical variables play a crucial role in understanding phenological stages and crop dynamics, optimizing ultimate agricultural practices, and achieving sustainable crop yields. This study examined the effectiveness of the Sentinel-2 Biophysical Processor (S2BP) in accurately estimating crop dynamics descriptors, including fractional vegetation cover (FVC), leaf area index (LAI), leaf chlorophyll a and b (LCab), and canopy water content (CWC). The evaluation was conducted using estimation quality indicators (EQIs) and comprehensive ground throughout the entire growing season at the field scale. To identify soil and vegetation pixels, the spectral unmixing technique was employed. According to the EQIs, the best retrievals were obtained for FVC in around 99.9% of the 23,976 pixels that were analyzed during the growth season. For LAI, LCab, and CWC, over 60% of the examined pixels had inputs that were out-of-range. Furthermore, in over 35% of the pixels, the output values for LCab and CWC were out-of-range. The FVC, LAI, and LCab estimates agreed well with ground measurements (R2 = 0.62–0.85), whereas a discrepancy was observed for CWC estimates when compared with ground measurements (R2 = 0.51). Furthermore, the uncertainties of FVC, LAI, LCab, and CWC estimates were 0.09, 0.81 m2/m2, 60.85 µg/cm2, and 0.02 g/cm2 through comparisons to ground FVC, LAI, Cab, and CWC measurements, respectively. Considering EQIs and uncertainty metrics, the order of the estimation accuracy of the four variables was FVC > LAI > LCab > CWC. Our analysis revealed that temporal variations of FVC, LAI, and LCab were primarily driven by field-scale events like sowing date, growing period, and harvesting time, highlighting their sensitivity to agricultural practices. The robustness of S2BP results could be enhanced by implementing a pixel identification algorithm, like embedding spectral unmixing. Overall, this study provides detailed, pixel-by-pixel insights into the performance of S2BP in estimating FVC, LAI, LCab, and CWC, which are crucial for monitoring crop dynamics in precision agriculture.

Research on the application of Bagging W-KNN algorithm in alloy steel identification with PXRF analyzer

In response to the inadequate alloy steel identification capability and intelligent recognition of domestic portable X-ray fluorescence analyzers, as well as the impact of existing classification algorithms due to factors such as imbalanced data distribution, this paper proposes the Bagging W-KNN intelligent identification algorithm for alloy steel grade. This method is based on the K-nearest neighbor classification algorithm, employs the Chi-Square statistical method, Bagging ensemble algorithm, introduces the Gini index from the decision tree classification algorithm, and utilizes the Gaussian function probability estimation method. Comparative experiments with four typical algorithms—Decision Tree, KNN, SVM, and Naive Bayes—demonstrate that the proposed algorithm achieves accuracy, precision, recall, and F1 values of 97.01%, 97.30%, 96.69%, and 96.18% respectively, significantly outperforming the other four algorithms and improving prediction accuracy.

Millimeter-Wave Radar-Based Identity Recognition Algorithm Built on Multimodal Fusion.

Millimeter-wave radar-based identification technology has a wide range of applications in persistent identity verification, covering areas such as security production, healthcare, and personalized smart consumption systems. It has received extensive attention from the academic community due to its advantages of being non-invasive, environmentally insensitive and privacy-preserving. Existing identification algorithms mainly rely on a single signal, such as breathing or heartbeat. The reliability and accuracy of these algorithms are limited due to the high similarity of breathing patterns and the low signal-to-noise ratio of heartbeat signals. To address the above issues, this paper proposes an algorithm for multimodal fusion for identity recognition. This algorithm extracts and fuses features derived from phase signals, respiratory signals, and heartbeat signals for identity recognition purposes. The spatial features of signals with different modes are first extracted by the residual network (ResNet), after which these features are fused with a spatial-channel attention fusion module. On this basis, the temporal features are further extracted with a time series-based self-attention mechanism. Finally, the feature vectors of the user's vital sign modality are obtained to perform identity recognition. This method makes full use of the correlation and complementarity between different modal signals to improve the accuracy and reliability of identification. Simulation experiments show that the algorithm identity recognition proposed in this paper achieves an accuracy of 94.26% on a 20-subject self-test dataset, which is much higher than that of the traditional algorithm, which is about 85%.

A VFM-based identification method for anisotropic thermal-mechanical properties of sheet metals using the digital image correlation and infrared thermography assisted heterogeneous test

The accurate characterization of the anisotropic thermal-mechanical constitutive properties of structural sheet metals at elevated temperatures and under nonuniform stress/strain states is crucial for the precise hot plastic forming and structural behavior evaluation of an engineering sheet part. Traditional thermal-mechanical testing methods rely on the assumption of states homogeneity, leading to a large number of tests required for the characterization of material anisotropy and nonlinearity at various high temperatures. In this work, a highly efficient identification method is proposed that allows the simultaneous characterization of the anisotropic yielding, strain hardening and elasto-plasticity thermal softening material properties using the minimum number of tests, releasing the limitations of traditional identification methods. This is implemented by performing a digital image correlation and infrared thermography assisted heterogeneous high temperature test and processing the full-field measurement data based on the principle of virtual work. A double-notched tensile specimen configuration combined with a center-to-periphery temperature gradient is designed first to enable the high heterogeneity of stress/temperature states. Simulations of the heterogeneous tests are then performed to supply idealized reference data that can be used to evaluate the identification sensitivity of the proposed identification algorithm. After the numerical validation of the methodology, it is then applied to the TC4 titanium alloy sheet specimen using the self-developed experimental setup. The experimental identification results verify that the multiple anisotropic thermal-mechanical elasto-plasticity constitutive parameters can be accurately identified from the heterogeneous test with high computation efficiency, significantly simplifying the testing process in comparison to applying multiple traditional homogeneous tests. The current work provides an effective and convenient alternative to high temperature identification strategies used by the material processing community.