Identification Method Research Articles

A Rapid Circuit Phase Error Identification and Compensation Method for MEMS QMG Achieving 99.7% Reduction in ZRO Drift

A Rapid Circuit Phase Error Identification and Compensation Method for MEMS QMG Achieving 99.7% Reduction in ZRO Drift

Spectral methods of identification of structural transformations in particles of polymineral complexes

Abstract In recent years, new deposits of polymineral complexes have been actively discovered and are beginning to be developed in the Orenburg region of Russia. Knowledge of the structural features of such heterogeneous systems will help provide a controlled structural evolution. Such studies will help shorten the time needed for the introduction of raw materials into production processes of functional ceramics. A selection of physical and chemical methods has been chosen to analyze natural polymineral complexes in order to understand the temperature evolution of their phase composition and structural transformations. The polymineral complexes from deposits in the Orenburg region of Russia are known to be fusible. Infrared spectroscopy and diffractometry methods have been used to identify six to ten crystalline phases in the studied objects. The main phases have been identified as follows: quartz, calcite, kaolinite, montmorillonite, clinochlore, and magnetite. Structural changes were induced by heating the sample to 1200 K at a rate of 10 K/min while recording derivatograms. EPR spectroscopy established that impurity centers have an ambiguous role in the evolution of natural mineral structures. They can both stabilize crystal lattices and cause distortions due to the Jahn-Teller effect. This is particularly true for layered lattices that contain octahedral motifs with a central impurity ion. It has been demonstrated that impurity centers Fe3+ and Mn2+ act as spin labels for structural transformations. As a result of the destruction of crystal components, these impurity centers form clusters with each other and become undetectable by other methods of analysis.

A fast impact force identification method via constructing a dynamic reduced dictionary

Impact force identification is an important aspect of structural health monitoring for online assessment of the integrity of engineering structures. Increasing the number of monitoring positions is undoubtedly beneficial to accurate force localization, however, it may increase sharply the dimension of the transfer matrix and thus lead to a large-scale inverse problem. In this case, traditional methods, such as the Iterative Shrinkage Threshold algorithm (IST) and the Two-Step Iterative Shrinkage Thresholding algorithm (TwIST), suffer from low solving efficiency. In this paper, the dynamic reduced dictionary is firstly introduced according to the sparsity of impact force in both time and spatial domains so as to lower significantly the dimension of the transfer matrix. Then, a new reduced unconstrained optimization problem is established via the dynamic reduced dictionary for impact force identification. By integrating the reduced unconstrained optimization problem into each iteration step of IST/TwIST, two novel algorithms, which are the reduced Newton iteration shrinkage threshold algorithm (rNIST) and the reduced Newton two-step iteration shrinkage threshold algorithm (rNTwIST), are proposed to solve quickly the large-scale impact force identification problem using ℓ1-norm sparse regularization. Meanwhile, the adaptive setting of regularization parameters strategy is used to construct force more fast and accurately. The proposed algorithms are numerically and experimentally demonstrated through identifying impact forces at 16-point and 81-point distributions of a plate using one sensor response. The results show that rNIST/rNTwIST can more quickly and accurately identify impact forces under numerous monitoring points compared with other ℓ1-norm regularization methods and nonconvex sparse regularization methods. Moreover, the more the number of monitoring points, the more obvious the efficiency improvement of the impact force identification by rNIST/rNTwIST method.

An investigation of scattered light integrating collector technology for rapid blood culture sensitivity testing.

Introduction. Sepsis rates are increasing, with Gram-negative organisms representing a large proportion of bloodstream infections. Rapid antibiotic administration, alongside diagnostic investigations, is required for the effective management of these patients.Gap statement. Current diagnostics take ~48 h for a final report; therefore, rapid diagnostics are required.Aim. This study investigated a novel antibiotic sensitivity method, the scattered light integrating collector (SLIC), combined with a rapid identification method using matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) technology to determine if an accurate identification and susceptibility result can be provided within 4 h of a positive blood culture report.Methodology. A total of 47 blood cultures containing Gram-negative bacteria from 46 patients were processed using the MALDI-TOF Biotyper Sepsityper for identification directly from the blood and the SLIC instrument for susceptibility testing. All organisms were also tested using the current standard workflow used in the host laboratory. Categorical agreement (CA), major errors (MaEs) and very major errors (VMEs) were determined.Results. SLIC produced susceptibility results with a 71.9% CA, 30.6% MaE and 17.5% VME. The median difference in time to the final result was 44.14 (43 : 05-45 : 15) h earlier compared to the current method.Conclusion. We conclude that SLIC was unable to consistently provide sufficiently accurate antibiotic susceptibility results compared to the current standard method.

Optimal Strictly Stealthy Attack Design on Cyber-Physical Systems: A Data-Driven Approach.

In this article, an issue of data-driven optimal strictly stealthy attack design for the stochastic linear invariant systems is investigated, with the aim of maximizing the system performance degradation under an energy bounded constraint and bypassing the parity-space-based attack detector. Importantly, the proposed attack policy refrains from the assumption that the system knowledge is known to attackers. A novel strictly stealthy attack sequence (SSAS), coordinating the sensor and actuator signals simultaneously, is proposed with a sufficient and necessary condition for the existence of such an attack presented. Specifically, the SSAS is parameterized as a vector in the null space of a specific matrix which is constructed by a parity matrix and the system Markov parameters. For the purpose of data-driven attack realization, modified subspace identification methods are utilized to achieve an unbiased estimation of the required parameters via the closed-loop data. On this basis, the attack design is formulated as a constrained optimization problem, an explicit solution to which is given to characterize the optimal strictly stealthy attack. Finally, the vulnerability of the cyber-physical systems is analysed from the perspective of the parameter selection for the parity space-based detector. A case study on a three-tank model verifies the efficiency of the proposed approach.

DC Bus Capacitance Identification Method Based on Frequency Domain Data Drive for EV Charging System

DC Bus Capacitance Identification Method Based on Frequency Domain Data Drive for EV Charging System

Fast Terminal Synergetic Speed Control for SMPMSM Drive System

For the surface‐mounted permanent magnet synchronous motor (SMPMSM) drive system, a fast terminal synergetic speed control (FTSSC) is proposed to improve its speed dynamic performance and robustness. In the proposed control scheme, the ultra‐local model of SMPMSM drive system, which considers motor parameter uncertainties and unknown disturbances, is set up based on the algebraic parameter identification method. Then, the nonlinear terminal manifold is designed to achieve speed rapid convergence in finite time, moreover, the fast synergetic speed control law that satisfies the voltage and current constraints of SMPMSM drive system is derived. Meanwhile, the Lyapunov function is designed to prove the stability of the proposed fast terminal synergetic speed‐controlled SMPMSM drive system. Finally, the effectiveness and feasibility of proposed control is verified through the experimental research of SMPMSM drive system. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Non-radical oxidation driven by iron-based materials without energy assistance in wastewater treatment.

Chemical oxidation is extensively utilized to mitigate the impact of organic pollutants in wastewater. The non-radical oxidation driven by iron-based materials is noted for its environmental friendliness and resistance to wastewater matrix, and it is a promising approach for practical wastewater treatment. However, the complexity of heterogeneous systems and the diversity of evolutionary pathways make the mechanisms of non-radical oxidation driven by iron-based materials elusive. This work provides a systematic review of various non-radical oxidation systems driven by iron-based materials, including singlet oxygen (1O2), reactive iron species (RFeS), and interfacial electron transfer. The unique mechanisms by which iron-based materials activate different oxidants (ozone, hydrogen peroxide, persulfate, periodate, and peracetic acid) to produce non-radical oxidation are described. The roles of active sites and the unique structures of iron-based materials in facilitating non-radical oxidation are discussed. Commonly employed identification methods in wastewater treatment are compared, such as quenching, chemical probes, spectroscopy, mass spectrometry, and electrochemical testing. According to the process of iron-based materials driving non-radical oxidation to remove organic pollutants, the driving factors at different stages are summarized. Finally, challenges and countermeasures are proposed in terms of mechanism exploration, detection methods and practical applications of non-radical oxidation driven by iron-based materials. This work provides valuable insights for understanding and developing non-radical oxidation systems.

Damage mechanism identification and failure behavior of 2D needle-punched SiCf/SiC composites based on acoustic emission and digital image correlation

In order to improve the reliability of damage analysis for SiCf/SiC composites, an identification method of damage mechanism was established by combining in-situ acoustic emission (AE) and digital image correlation (DIC). The corresponding failure behavior of 2D needle-punched SiCf/SiC composites during ambient-temperature tensile test was investigated in detail. Through a machine learning k-means algorithm, AE signals could be effectively divided into five clusters: friction and sliding, interface damage, matrix cracking, individual fiber breaks and collective fiber breaks. DIC results show that the surface strain of composites increased non-uniformly during the tensile process, and the architecture of the composites had a significant influence on the initiation and propagation of cracks. To summarize, the tensile process consisted of three stages: the elastic stage, the rapid propagation of matrix cracks, the coordinated fiber fracture within the large strain bands. The failure of composites was dominated by the limited load transferring ability of the interface.

Modulation Format Identification Method Based on Multi-Feature Input Hybrid Neural Network

Modulation Format Identification Method Based on Multi-Feature Input Hybrid Neural Network

Enhanced cancer classification and critical feature visualization using Raman spectroscopy and convolutional neural networks

Cell misuse and cross-contamination pose a significant threat to the accuracy of cell research outcomes, often leading to the wasteful expenditure of time, manpower, and material resources. Consequently, the accurate identification of cell lines is paramount. However, traditional identification methods, which often involve staining and culturing procedures, are not only time-consuming but also laborious. This underscores the need for a novel approach that enables rapid and automated cell line identification, thereby enhancing research efficiency and accuracy. Raman spectroscopy, renowned for its label-free, rapid, and noninvasive nature, offers invaluable molecular insights into samples, making it a widely utilized technique in the biological field. In this study, the identification of one normal and five cancer cell lines was achieved using a sparrow search algorithm-convolutional neural networks (SSA-CNN), considering both the full spectra and fingerprint region perspectives. The SSA-CNN model demonstrated exceptional performance, not just in binary classification, but also in accurately distinguishing among six cell lines. It achieved the highest accuracy (around 95 %), and the lowest standard error (≤3%), for both the full spectra and fingerprint region. Based on the highly accurate SSA-CNN model, proposed the application of gradient-weighted class activation mapping (Grad-CAM) to visualize the Raman feature peaks. Upon comparing the visualized Raman features with reported biomarkers, found that not only were common biomolecules such as glucose, proteins, and liquids visualized, but specific feature peaks also aligned with reported biomarkers. The aforementioned results clearly demonstrated that the proposed strategy not only classifies cancer cell lines with remarkable accuracy but also served as a valuable tool for the discovery of novel biomarkers.

Study on the Internal Flow Characteristics of High Flowrate Emergency Drainage Series-parallel Pump

Abstract Floods have become the main natural disaster threatening the safety of peoples lives and property. It is of great significance to develop high flow emergency drainage equipment. An innovative structure of pumps with high flow rate and multiple operating conditions is proposed in this paper to achieve series and parallel operation. It established an automatic optimization platform that integrates functions such as 3D modeling, mesh generation, computational fluid dynamics, and scheme optimization, and developed an efficient hydraulic model for a volute mixed flow pump combining flow field diagnostic techniques. The research results indicate that the inlet distortion of the secondary stage pump is the main cause of pump performance loss for both series and parallel operation condition. The third-generation vortex identification method was used to diagnose the internal flow of the pump under two operating conditions and control strategies were proposed. The final optimization scheme show that the pump can reach 1800 m3/h with a head of 9.6m and efficiency of 80% in parallel operation condition. In series operation condition, the pump can reach 900 m3/h with a head of 18.3m and an efficiency of 75.3%.

Analysis and mitigation of uncertainties in damage identification by modal-curvature based methods

The objective of this paper is to address and reduce the uncertainties associated with measurement noise and discretization in damage identification methods based on modal curvature analysis. The experimental case study considers the local reduction in the bending stiffness of a slender beam under free-free and simply supported boundary conditions. First, the analysis of error sources and their propagation is theoretically set up. Second, the mitigation of uncertainties in damage localization is pursued using a two-stage approach based on multiple hypothesis testing relative to the normalized indices and the definition of a combined macro-index. Finally, the Monte Carlo method is exploited to obtain the statistical error distribution of the experimental damage position and severity predictions by randomizing the numerical displacement mode shapes with the identified noise. The present analysis allows us to find the optimal number of sensors that minimizes the combination of bias and truncation errors, to highlight how sensor spacing and data noise affect damage localization, and to determine the uncertainty bounds of the predicted damage severity. The two-stage approach, enhanced by selecting thresholds related to real noise levels and tuned on SHM objectives, appears to improve identification accuracy compared to the separate use of damage indices based on absolute confidence levels.

Application of terahertz time-domain spectroscopy and chemometrics-based crested porcupine algorithm in identification of different medicinal parts of Angelica sinensis

ObjectiveAngelica sinensis is one of the commonly used Chinese herbal medicine in traditional Chinese medicine clinic, exhibits different pharmacological characteristics due to variations in the content of active ingredients in its head, body, and tail. Therefore, research on the identification methods of different medicinal parts of Angelica sinensis is of great practical significance. Terahertz Time-Domain Spectroscopy (THz-TDS) technology is widely used in the field of nondestructive testing because of its unique electromagnetic wave characteristics. This study explores the feasibility of combining THz-TDS with chemometrics to identify different medicinal parts of Angelica sinensis. MethodsBy comparing the spectral response characteristics of different parts of Angelica sinensis to various optical parameters, the absorption coefficient spectrum in the 0.6–3.0 THz range was selected, and three types of feature extraction algorithms, namely, joint Competitive Adaptive Reweighted Sampling (CARS), Uninformative Variable Elimination (UVE), and Successive Projections Algorithm (SPA), were used to establish the classification models of Extreme Learning Machine (ELM), Random Forest (RF), and Support Vector Machine (SVM) in turn, and optimize the models by using the crown porcupine algorithm (Crested Porcupine Optimizer (CPO) to optimize the model. ResultsThe research results indicate that the CPO optimizer significantly improved the classification accuracy of the models, with the accuracy of the ELM, RF, and SVM models increasing by 4.36%, 1.11%, and 12.22%, respectively. The SPA-CPO-SVM model exhibited the best overall performance, achieving accuracies of 96.11% and 97.96% on the prediction and training sets, respectively, while the number of input features was only 5% of the total feature set. ConclusionThe results show that the fully joint feature extraction strategy and optimization algorithm can play a powerful synergistic effect in model construction, confirming the feasibility of THz-TDS technology to correctly identify different medicinal parts of Angelica sinensis, and providing an important reference for the application of terahertz technology in the identification of Chinese herbal medicines.

A novel method for identifying key nodes in multi-layer networks based on dynamic influence range and community importance

Identifying key nodes in multi-layer networks is a hot research topic in complex network science and has broad application prospects, such as in mining enterprises that significantly affecting multi-layer industrial chains. Unlike single-layer networks, nodes in multi-layer networks exhibit heterogeneity due to varying connections and locations. There are also correlations between different network layers, which is particularly evident in industrial chains where companies operate across multiple layers of production, supply and distribution. It is necessary to consider the impact of these layers on the global performance of key node identification. In addition, due to changes in connections, the community structure of each network layer should be different, reflecting the dynamic nature of industrial collaborations and partnerships. However, existing research lacks the model that addresses the above problems. Therefore, this paper proposes a key node identification method based on Dynamic Influence Range and Community Importance (DIRCI), using both local and global information of the multi-layer network simultaneously. DIRCI determines the importance of nodes through three centrality measures: dynamic influence range-based centrality, network layer centrality and community-based centrality. Dynamic influence range-based centrality models node heterogeneity by combining the influence range of nodes and their neighbors with lower computational costs. Network layer centrality captures the corresponding importance for different network layers. Community-based centrality comprehensively considers the importance of community, the importance of each node within the community and between different communities. Experimental results for nineteen multi-layer networks show that DIRCI achieves better performance of key node identification than the latest algorithms.

Research on Rapid Detection Method of Cavitation State of Water jet Propulsion Pump Based on Multi-source and Multi-feature of Acoustic Emission Signal and Bayesian Network

Abstract Cavitation prevents the conversion of internal dynamic and pressure energy in a water jet propulsion pump and causes erosion of flow passage components. Acoustic emission is a non-destructive test method for instability signals. Based on the incipient mechanism and development process of cavitation, three acoustic emission signal feature parameters (ring counting rate, energy release rate, Hurst index) are proposed to define the cavitation state utilizing the waveform pattern and signal self-similarity. The multi-source and multi-feature cavitation identification model of acoustic emission signals is established through cavitation simulation, multi-monitoring point acoustic emission signal testing, and feature parameter extraction, and a large number of probabilistic trainings are carried out using Bayesian network, resulting in a fast cavitation state identification method based on acoustic emission signals. The research results have important practical engineering application significance for the prediction of cavitation resistance, real-time dynamic monitoring of cavitation phenomenon and prevention of cavitation damage.

C-type two-thermocouple sensor design between 1000 and 1700 °C.

At the present stage of transient ultra-high-temperature energy release of boron-containing warm-pressure explosives, single thermocouples are often used for multi-point measurements in the process of their temperature field changes, and the results of their temperature field reconstruction are not satisfactory due to the limited consistency of the thermocouples. Aiming at the above-mentioned problems, a C-type two-thermocouple suitable for transient temperature measurement in high-temperature environments is designed; the system characterization of the two-thermocouple is carried out by using the blind system identification method of the inter-relationships; the identification process is evaluated by a new cost function; and the optimal solution on the new cost function is realized by using the gradient descent method. The temperature reconstruction of the two-thermocouple output excited by the simulated heat source is carried out by using the auto-regressive with extra inputs model, and the feasibility of the reconstruction results is verified. In the experimental part, a thermocouple dynamic characteristic calibration system based on a high-temperature furnace is constructed and experimental validation is carried out in the high-temperature furnace to compare the effects of different exposure lengths and different wire diameters on the output of the two-thermocouple, and as a result, the outputs are corrected and analyzed. The results show that two-thermocouple methods with different combinations of wire diameters are better for temperature measurement, with a reconstructed root mean squared error of 0.0162 and a goodness of fit of 89.13%.

Novel Parameter Identification Method of Extended Debye Model for Long-Length Submarine Cables

Novel Parameter Identification Method of Extended Debye Model for Long-Length Submarine Cables

Radio frequency interference identification using dual cross-attention and multi-scale feature fusing

Radio astronomy plays a very important role in promoting scientific progress and unraveling the mysteries of the universe. However, radio telescopes are inevitably affected by radio frequency interference (RFI) when receiving radio signals, which leads to a reduction in data quality and has a serious impact on the formation of correct scientific conclusions. Therefore, it is essential to identify the RFI present in the observational data. In order to effectively identify RFI, improve the existing RFI identification methods that suffer from missed detections, and enhance the performance of RFI identification, this paper proposes a novel method that combines a dual cross-attention mechanism with multi-scale feature fusion. Experimental studies were conducted using the observational data from the 40-meter radio telescope at the Yunnan Astronomical Observatory of the Chinese Academy of Sciences. The proposed method achieved scores of 92.49%, 83.90%, and 87.99% in terms of precision, recall, and F1−score, respectively. It outperformed existing methods (U-Net, RFI-Net, R-Net6, RFI-GAN, EMSCA-UNet) in recall and F1−score, effectively reducing the occurrence of missed detections and improving the overall performance of radio frequency interference identification.

LC-MS/MS Profiling of the Poisonous Wild Mushroom, Entoloma mastoideum, (Entolomataceae, Basidiomycota) in Sabah (Northern Borneo), Malaysia

In Malaysia, mushroom poisoning poses a significant public health concern, particularly as local communities frequently consume wild mushrooms without fully recognizing the potential dangers they present. Ingestion of wild mushrooms, which resemble edible species but contain deadly chemicals, is the main cause of poisoning risk. The genus Entoloma (Fr.) P. Kumm is dispersed worldwide, with over 1,500 species recorded. Entoloma mastoideum, a poisonous mushroom, was recorded for the first time at Kota Marudu district, Sabah, Malaysia, where ten cases of mushroom poisoning outbreaks were recorded during the rainy season of October 2019. The morphological characters of the specimens were documented and their microscopic features were analyzed using compound microscope and scanning electron microscope. A phylogenetic tree was constructed using Maximum likelihood and Bayesian analysis. Chemical profiling of the poisonous mushroom specimen was done via liquid chromatograph mass spectrometry (LC-MS/MS) to identify toxic metabolites. The phylogenetic analysis showed that the Bornean E. mastoideum is closely related to the Chinese isolates (100% BS/1.0 PP). LC-MS/MS profiling detected a total of 162 metabolites that were classified into ten general groups, where several toxic compounds were detected amongst the aromatics, essential amino acids, and fatty acid derivatives. The toxic compounds identified in the mushroom extract, including amino acid derivatives such as 3,4,5,6-tetrahydroxyynorleucine, valpromide, and betaine, have been reported to cause neurotoxicity, cardiotoxicity, liver damage, and gastrointestinal harm. The presence of these toxic compounds underscores the need for caution when consuming wild mushrooms. Further research on poisonous Entoloma species is vital for developing accurate identification methods and understanding their toxic potential. This knowledge is essential for enhancing public awareness, preventing mushroom poisoning incidents and safeguarding public health.