Fast Identification Algorithm Research Articles

Electron ionization mass spectrometry feature peak relationships combined with deep classification model to assist similarity algorithm for fast and accurate identification of compounds.

Gas chromatography-mass spectrometry (GC-MS) combines chromatography and MS, providing full play to the advantages of high separation efficiency of GC, strong qualitative ability of MS, and high sensitivity of detector. In GC-MS data processing, determining the experimental compounds is one of the most important analytical steps, which is usually realized by one-to-one similarity calculations between the experimental mass spectrum and the standard mass spectrum library. Although the accuracy of the algorithm has been improved in recent years, it is still difficult to distinguish structurally similar mass spectra, especially isomers. At the same time, the library capacity is very large and increasing every year, and the algorithm needs to perform large numbers of calculations with irrelevant compounds in the library to recognize unknown compounds, which leads to a significant reduction in efficiency. This work proposed to exclude a large number of irrelevant mass spectra by presearching, perform preliminary similarity calculations using similarity algorithms, and finally improve the accuracy of similarity calculations using deep classification models. The replica library of NIST17 is used as the query data, and the master library is used as the reference database. Compared with the traditional recognition algorithm, the preprocessing algorithm has reduced the time by 4.2h, and by adding the deep learning models 1 and 2 as the final determination, the recognition accuracy has been improved by 1.9% and 6.5%, respectively, based on the original algorithm. This method improves the recognition efficiency compared to conventional algorithms and at the same time has better recognition accuracy for structurally similar mass spectra and isomers.

Research on rapid extraction of internal resistance of lithium battery based on short-time transient response

Unlike laboratory measurements and online parameter observations based on long-term continuous operation data, offline fast measurement of battery parameters is widely used for convenient and accurate acquisition of battery parameters in battery production and maintenance processes. However, too short allowable measurement time in engineering results in a small amount of dynamic information that the algorithm can obtain and unclear initial state values, making it difficult to improve the accuracy of the algorithm. This paper employs a local coordinate system established in the vicinity of the measurement moment to extract the transient behavior of battery terminal voltage response under excitation current, and investigates the characterization capability of these transients on ohmic and polarization internal resistance of batteries at different time points. Moreover, based on an equivalent circuit model, it evaluates how initial values of transient voltage affect accuracy in calculating internal resistance. Through customized working condition verification, the selection principles for rapid identification of time points and the basic requirements for excitation conditions have been determined. Comparative verification of online parameter identification results based on continuous time series demonstrates that the fast identification algorithm can accurately identify Ohmic internal resistance and effectively characterize the variation law of battery polarization.

Eulerian fast motion identification algorithm for deformation measurement of cable-stayed bridge

Eulerian fast motion identification algorithm for deformation measurement of cable-stayed bridge

A Bayesian Nuclide Identification Algorithm Based on the Energy Spectrum

A fast and reliable nuclide identification algorithm is important to the detection of radioactive materials, and the emergence of a sequential Bayesian algorithm provides a new direction of research on radioactive detection. This article proposes a Bayesian nuclide identification algorithm based on the energy spectrum and deploys the algorithm on a microprocessor for real-time online processing. The algorithm builds a probability model based on the natural background radiation and radioactive source radiation and a decision function based on sequence testing. The processor conducts a posteriori estimation on the collected energy spectrum at fixed time intervals and updates the decision function. A laboratory self-developed CeBr3 detector was used in testing. Four radioactive sources, namely 241Am, 133Ba, 137Cs, and 60Co, were used in experiments with fixed and moving radioactive sources. The experimental results show that the proposed algorithm can rapidly identify mixed and mobile radioactive sources.

Holistic Optimal Design of Face-Milled Hypoid Gearsets

Abstract The aim of this paper is to present a holistic framework to design optimized spiral bevel and hypoid gearsets with accurate finite element simulations in the loop. Starting from the basic transmission data, we first size gear and pinion blanks, and then we synthesize the basic machine-tool settings required to generate the two toothed members. This first step represents the macro-geometry design phase and its outcome is a conjugate spiral bevel or hypoid gearset. The second design phase is represented by the definition of the optimal pinion micro-geometry. This is formulated as a multi-objective optimization problem (MOOP) where the obtained optimal ease-off is guaranteed to be manufacturable. To this end, an original strategy is proposed where the search for the pinion optimal tooth surface happens in the space of the coefficients of a polynomial representation of its micro-topography. However, thanks to a fast identification algorithm that can handle all the higher-order motions, the ideal ease-off is projected onto set of machine-tool settings, thus ensuring manufacturability from the outset. It is worth remarking that the objective functions in the MOOP are evaluated by calling as a back-end solver one of the most accurate loaded tooth contact analysis software available on the market. A dedicated parallel implementation of such MOOP allows to maintain computation times within very reasonable limits. A fully worked out numerical test case clearly demonstrates that the whole procedure far surpasses the current state of the art.

Fast Identification of Vulnerable Set for Cascading Failure Analysis in Power Grid

Past 20 years has witnessed some exorbitant fallout and large-scale blackouts in power system, particularly due to cascading failures and their propagation in the crucial yet complex and networked infrastructure. A pivotal prevention measure to steer clear from cascading events is the identification of vulnerable set, defined as the composition of specific line combinations that can trigger sequence of errors. By nature, the identification problem is NP-hard and a resort to approximation algorithms is necessary. In this article, we first construct a general yet rigorous formalism for the mathematical analysis of cascading failure in networked systems. With a tailored treatment of the propagation mechanism, a fast identification algorithm (FIA) for vulnerable set is then designed based on a key observation revealing the correlation structure among different N- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> contingencies. By analyzing the monotonic nondecreasing, quasi-submodular property of the propagation process, a theoretical lower bound of our algorithm is given in specific order. Besides, we show that the optimization framework of our algorithm can be readily extended to incorporate prior information. Numerical experiments on IEEE 30-, 118-, and 200-bus systems are performed to verify the effectiveness and efficiency of both FIA and its optimization framework.

Meta-learning of personalized thermal comfort model and fast identification of the best personalized thermal environmental conditions

The model of personalized thermal comfort can be learned via various machine learning algorithms and used to improve the individuals’ thermal comfort levels with potentially less energy consumption of HVAC systems. However, the learning of such a model typically requires a substantial number of thermal votes from the considered occupant, and the environmental conditions needed for collecting some votes may be undesired by the occupant in order to obtain a model with good generalization ability. In this paper, we propose to use a meta-learning algorithm to reduce the required number of personalized thermal votes so that a personalized thermal comfort model can be obtained with only a small number of feedback. With the learned meta-model, we derive a method based on the backpropagation of neural networks to quickly identify the best environmental and personal conditions for a specific occupant. The proposed identification algorithm has an additional advantage that the thermal comfort, indicated by the mean thermal sensation value, improves incrementally during the data collection process. We use the ASHRAE global thermal comfort database II to verify that the meta-learning algorithm can achieve an improved prediction accuracy after using 5 thermal sensation votes from an occupant to make adaptations. In addition, we show the effectiveness of the fast identification algorithm for the best personalized thermal environmental conditions with a thermal sensation generation model built from the PMV model.

A regularised fast recursive algorithm for fraction model identification of nonlinear dynamic systems

The fraction model has been widely used to represent a range of engineering systems. To accurately identify the fraction model is however challenging, and this paper presents a regularised fast recursive algorithm (RFRA) to identify both the true fraction model structure and the associated unknown model parameters. This is achieved first by transforming the fraction form to a linear combination of nonlinear model terms. Then the terms in the denominator are used to form a regularisation term in the cost function to offset the bias induced by the linear transformation. According to the structural risk minimisation principle based on the new cost function, the model terms are selected based on their contributions to the cost function and the coefficients are then identified recursively without explicitly solving the inverse matrix. The proposed method is proved to have low computational complexity. Simulation results confirm the efficacy of the method in fast identification of the true fraction models for the targeted nonlinear systems.

Random forest microplastic classification using spectral subsamples of FT-IR hyperspectral images.

In this work, a random decision forest model is built for fast identification of Fourier-transform infrared spectra of the eleven most common types of microplastics in the environment. The random decision forest input data is reduced to a combination of highly discriminative single wavenumbers selected using a machine learning classifier. This dimension reduction allows input from systems with individual wavenumber measurements, and decreases prediction time. The training and testing spectra are extracted from Fourier-transform infrared hyperspectral images of pure-type microplastic samples, automatizing the process with reference spectra and a fast background correction and identification algorithm. Random decision forest classification results are validated using procedurally generated ground truth. The classification accuracy achieved on said ground truths are not expected to carry over to environmental samples as those usually contain a broader variety of materials.

A fast and efficient algorithm for DNA sequence similarity identification

DNA sequence similarity analysis is necessary for enormous purposes including genome analysis, extracting biological information, finding the evolutionary relationship of species. There are two types of sequence analysis which are alignment-based (AB) and alignment-free (AF). AB is effective for small homologous sequences but becomes NP-hard problem for long sequences. However, AF algorithms can solve the major limitations of AB. But most of the existing AF methods show high time complexity and memory consumption, less precision, and less performance on benchmark datasets. To minimize these limitations, we develop an AF algorithm using a 2D k-mer count matrix inspired by the CGR approach. Then we shrink the matrix by analyzing the neighbors and then measure similarities using the best combinations of pairwise distance (PD) and phylogenetic tree methods. We also dynamically choose the value of k for k-mer. We develop an efficient system for finding the positions of k-mer in the count matrix. We apply our system in six different datasets. We achieve the top rank for two benchmark datasets from AFproject, 100% accuracy for two datasets (16 S Ribosomal, 18 Eutherian), and achieve a milestone for time complexity and memory consumption in comparison to the existing study datasets (HEV, HIV-1). Therefore, the comparative results of the benchmark datasets and existing studies demonstrate that our method is highly effective, efficient, and accurate. Thus, our method can be used with the top level of authenticity for DNA sequence similarity measurement.

Application of Fast P2P Traffic Recognition Technology Based on Decision Tree in the Detection of Network Traffic Data

With the rapid development of large-scale enterprise informatization construction, the network scale has become huge and complex, and the data traffic carried by the network is increasing. Accurate network traffic identification is the basis of network management and is of great significance to enterprise informatization construction and operation and maintenance. In response to the network operation and maintenance requirements of large enterprises, this paper analyzes the network architecture and network traffic distribution of large enterprise groups from the perspective of enterprise network operators and introduces the current operation and maintenance process of enterprise network performance failures. Maintenance process optimization and reengineering are carried out to plan and find out the shortcomings of the current process links and put forward corresponding solutions. Based on the research of traffic identification in recent years, a fast P2P network traffic anomaly identification algorithm based on decision tree model is proposed, which improves the efficiency and accuracy of network traffic application identification and network traffic anomaly data identification.

Pose error identification algorithm based on hemispherical resonant gyroscope assembly capacitance uniformity

The assembly performance of hemispherical resonant gyroscope (HRG) is directly affected by the spatial pose of the resonator and the electrode carrier, which can be reflected in the capacitance uniformity. This paper proposed a fast identification algorithm for pose error based on HRG assembly capacitance uniformity. A forward mathematical model for calculating capacitance from the resonator’s pose was established and verified by experiments and simulations. The effect of pose on the capacitance was analyzed by using the mathematical model. Based on the data of pose and capacitance obtained from the mathematical model, the capacitance-pose inverse model was constructed by back propagation (BP) neural network. This novel algorithm provides a swift way to identify the pose error of resonator and achieves capacitance uniformity in HRG assembly, which can significantly improve the assembly quality and efficiency of HRG.

A Fast Star Identification Algorithm of Star Sensors in the LIS Mode

In the South Atlantic Anomaly (SAA) area, due to the influence of transient noise caused by space radiation, a star sensor can easily stay in the lost-in-space (LIS) mode for a long time. To solve this problem, this paper proposes a fast star identification (FSI) algorithm. First, a noise suppression method based on scale assessment and neighborhood comparison is developed. Next, a fast and accurate search technique of multiple main stars based on the k-vector technique is used to realize star identification. The search technique builds a self-defined attribute database of stars, and a fast search method of a repeated star identity is proposed to realize the positioning of the main star. Lastly, the final main stars are obtained through the comparison of field of view and verification of angular distance. The experimental results showed that when the star sensor works at a speed of 0.1°/s and the level of transient noise signals is lower than 900, the successful identification rate is higher than 70%. In addition, compared with the triangle algorithm, match group algorithm, and multi-pole algorithm (MPA), the proposed FSI algorithm has the advantages of a higher successful identification rate and a faster execution speed.

A deep-learning approach for online cell identification and trace extraction in functional two-photon calcium imaging

In vivo two-photon calcium imaging is a powerful approach in neuroscience. However, processing two-photon calcium imaging data is computationally intensive and time-consuming, making online frame-by-frame analysis challenging. This is especially true for large field-of-view (FOV) imaging. Here, we present CITE-On (Cell Identification and Trace Extraction Online), a convolutional neural network-based algorithm for fast automatic cell identification, segmentation, identity tracking, and trace extraction in two-photon calcium imaging data. CITE-On processes thousands of cells online, including during mesoscopic two-photon imaging, and extracts functional measurements from most neurons in the FOV. Applied to publicly available datasets, the offline version of CITE-On achieves performance similar to that of state-of-the-art methods for offline analysis. Moreover, CITE-On generalizes across calcium indicators, brain regions, and acquisition parameters in anesthetized and awake head-fixed mice. CITE-On represents a powerful tool to speed up image analysis and facilitate closed-loop approaches, for example in combined all-optical imaging and manipulation experiments.

Pulsar identification method based on adaptive grey wolf optimization algorithm in X-ray pulsar-based navigations

Fast and accurate identification of pulsar signals is important for X-ray pulsar-based navigation (XNAV). Traditional pulsar signal detection technology based on FFT search and epoch folding (EF) requires a very long time to obtain an appropriate signal-to-noise ratio (SNR) gain, especially for weak pulsar signals with low photon fluxes. This paper proposes an adaptive stochastic resonance (SR) method based on the grey wolf optimizer (GWO) algorithm for fast pulsar identification. The GWO algorithm can optimize the SR parameters to obtain the optimal SR output that matches the input signal. Meanwhile, the initial population of GWO is generated by Chebyshev chaotic maps, which guarantees the diversity of the initial population and enhances the global search capability of GWO. To further improve the performance of SR, we add an extra denoising process, including a high-pass filter and singular value decomposition (SVD) that determines the effective singular components through dispersion entropy (DE), to suppress the noise to a great extent. We use simulation data and Rossi X-ray Timing Explorer (RXTE) observation data to verify the proposed method. The output SNR is greatly improved, and the method can identify pulsars in a short time. The results indicated that the proposed method has great practical value in engineering. Further experiments reveal the importance of SVD, DE and the Chebyshev chaotic map for the algorithm.

A parameter identification scheme for the orthotropic Poly6 yield function satisfying the convexity condition

The yield surface is the boundary of the elastic domain of a material and determines, via the normality rule, the direction of plastic deformation. Therefore, an accurate model of the yield surface of a material is crucial for the numerical simulation of its deformation processes. In addition, the yield surface must be convex in order to ensure a one-to-one relationship between the plastic strain rate and a corresponding stress state. In this context, homogeneous polynomial functions, in their most general form, have been shown to have a wide modeling range. However, it is also well known that these functions are not convex by default and hence the material parameters (the polynomial coefficients) must be subject to additional convexity constraints. Here we show that Poly6, the sixth order polynomial, features complete decoupling between pure shear and uniaxial traction. This allows us to devise a simple least squares optimization scheme based on biaxial and uniaxial test data, and an additional set of convexity control points which leads to a fast and explicit identification algorithm (Supporting code at https://github.com/stefanSCS/Poly6).

Unsupervised Identification of Surgical Robotic Actions From Small Non-Homogeneous Datasets

Robot-assisted surgery is an established clinical practice. The automatic identification of surgical actions is needed for a range of applications, including performance assessment of trainees and surgical process modeling for autonomous execution and monitoring. However, supervised action identification is not feasible, due to the burden of manually annotating recordings of potentially complex and long surgical executions. Moreover, often few example executions of a surgical procedure can be recorded. This letter proposes a novel fast algorithm for unsupervised identification of surgical actions in a standard surgical training task, the ring transfer, executed with da Vinci Research Kit. Exploiting kinematic and semantic visual features automatically extracted from a very limited dataset of executions, we are able to significantly outperform state-of-the-art results on a dataset of non-expert executions (58% vs. 24% F1-score), and improve performance in the presence of noise, short actions and non-homogeneous workflows, i.e. non repetitive action sequences.

A rapid system identification method for measuring explosion heat by the constant temperature method

In order to reduce the risk of measurement failure caused by system failure in the classical constant temperature method for measuring explosion heat, a measurement method was developed for identifying the explosion heat value based on the water temperature rise curve of an inner barrel before failure. Firstly, the heat transfer mechanism of the measurement process was analyzed, and the heat transfer model of the calorimeter was established, and the water temperature rise curve of the inner barrel in each measurement stage was obtained. Then, based on the system identification theory, the identification algorithm of intermediate parameters was proposed, and based on the idea of isolating easily oscillating parameters, a fast system identification algorithm was given to correct the temperature rise and explosion heat. And it is proved that the identification value of explosion heat approximately converges to the classical value. Finally, the test data of 8 explosive samples with explosion heat values ranging from 4 to 9 kJ/g were used to test the algorithm, and the test criterion for judging the convergence time was put forward. The simulation results show that the identification algorithm can effectively isolate the influence of the oscillation parameters and has a strong prediction ability on the temperature change of the water in the inner barrel, and that the identification value of explosion heat can quickly and stably converge to the upper limit of relative error of 3.5% within 40 min (1/3 of the main end stage), and that the test criterion can accurately judge the convergence time of the identified value of explosion heat. This method can also be extended to the adiabatic method for calculating explosion heat.

A Fast Search-Free Algorithm for Star Sensor Frame Identification by Star Configurations. A Version of Onboard Implementation

A Fast Search-Free Algorithm for Star Sensor Frame Identification by Star Configurations. A Version of Onboard Implementation

Faulted-Phase Identification in TCSC-Compensated Transmission Lines using Concordance Correlation Coefficient-Based Method

Fast fault detection in the Thyristor-controlled series capacitors “TCSC” compensated transmission line is the most challenging for many of the conventional fault detection methods. This paper proposes a new algorithm for fast fault identification in TCSC-compensated line based on concordance correlation of the current signals during the fault interval with corresponding current signals at the pre-fault interval. It is observed that the concordance correlation of the current signals at the present state with the same current signals at the previous state (just a few-cycle past) is very-strong during the health state and very weak when there is a fault state. Based on this and with the assistance of the cumulative approach, a fault detection index “” has been achieved. The proposed method has been tested through different fault circumstances including different compensation levels of TCSC device. Also, the proposed algorithm has been verified successfully with close in fault, high impedance fault, different sampling frequency, sudden load switching, load flow change and multi-machine system. The experimental results shown that the proposed method is good in terms of time response regardless of the compensation level, and therefore it is more efficient for series-compensated transmission lines protection.