Fast Identification Method Research Articles

Papaver somniferum and Papaver rhoeas Classification Based on Visible Capsule Images Using a Modified MobileNetV3-Small Network with Transfer Learning.

Traditional identification methods for Papaver somniferum and Papaver rhoeas (PSPR) consume much time and labor, require strict experimental conditions, and usually cause damage to the plant. This work presents a novel method for fast, accurate, and nondestructive identification of PSPR. First, to fill the gap in the PSPR dataset, we construct a PSPR visible capsule image dataset. Second, we propose a modified MobileNetV3-Small network with transfer learning, and we solve the problem of low classification accuracy and slow model convergence due to the small number of PSPR capsule image samples. Experimental results demonstrate that the modified MobileNetV3-Small is effective for fast, accurate, and nondestructive PSPR classification.

Fast Identification Method of Mine Water Source Based on Laser-Induced Fluorescence Technology and Optimized LSTM

There is a great threat to the production safety of coal mines caused by mine water disasters. Traditional identification methods are not adapted to the efficiency of today’s coal mining and do not offer the advantage of accurate detection in real-time. In this study, the Mayfly Algorithm (MA) was used to optimize the Long Short-Term Memory (LSTM) network, combined with laser-induced fluorescence technology, to apply it to the identification of mine water sources for the prevention of mine water disasters and post-disaster relief work. Taking sandstone water and goaf water as the original samples, five mixed water samples were also prepared by mixing the sandstone water and goaf water in different proportions, giving a total of seven water samples to be tested. Laser-induced fluorescence technology was used to obtain the fluorescence spectral data of water samples, and then the Linear Discriminant Analysis (LDA) dimensionality reduction algorithm and the Principal Component Analysis (PCA) dimensionality reduction algorithm were used to reduce the dimensions of the original spectral data. Then, three architectures, including LSTM, GA-LSTM (optimization of the LSTM by genetic algorithm) and MA-LSTM were designed to identify mine water sources. Finally, from the results’ analysis, MA-LSTM performs best in many aspects after PCA dimensionality reduction and has the best identification effect. These results supported the feasibility of the novel method.

Fast Identification of Micro-Health Parameters for Retired Batteries Based on a Simplified P2D Model by Using Padé Approximation

Better performance consistency of regrouped batteries retired from electric vehicles can guarantee the residual value maximized, which greatly improves the second-use application economy of retired batteries. This paper develops a fast identification approach for micro-health parameters characterizing negative electrode material and electrolyte in LiFePO4 batteries on the basis of a simplified pseudo two-dimensional model by using Padé approximation is developed. First, as the basis for accurately identifying micro-health parameters, the liquid-phase and solid-phase diffusion processes of pseudo two-dimensional model are simplified based on Padé approximation, especially according to enhanced boundary conditions of liquid-phase diffusion. Second, the reduced pseudo two-dimensional model with the lumped parameter is proposed, the target parameters characterizing negative electrode material (εn, Ds,n) and electrolyte (De, Ce) are grouped with other unknown but fixed parameters, which ensures that no matter whether the target parameters can be achieved, the corresponding varying traces is able to be effectively and independently monitored by lumped parameters. Third, the fast identification method for target micro-health parameters is developed based on the sensitivity of target parameters to constant-current charging voltage, which shortens the parameter identification time in comparison to that obtained by other approaches. Finally, the identification accuracy of the lumped micro-health parameters is verified under 1 C constant-current charging condition.

Establishing a Robust Model Using Stacked Generalization for Gas Identification of Coal-Bearing Strata in Block A, Northeastern Ordos Basin, China

Coal measure gas is a research hotspot in recent years. And yet the complexity of source-reservoir relationships and the ambiguity of the gas/water interface in coal measure reservoirs bring challenges to the traditional gas identification methods. With the development of intelligent computing, machine learning has shown good development prospects in the field of oil and gas exploration and development. However, on the one hand, the more capable the learning algorithm is, the greater the demand for data; on the other hand, traditional learning methods suffer from difficulties in hyperparameter tuning and generalization improvement when learning samples are insufficient. To perform intelligent and reliable gas identification in the coal measure reservoir, an ensemble learning-based gas identification method was proposed. The method models a two-layer structure. The first layer consists of multiple models that were trained by different learning algorithms, such as k-nearest neighbor (kNN), decision tree (DT), neural network (NN), and support vector machine (SVM). While the second layer was used to relearn the output of the first layer, which was implemented by logistic regression (LR). We tested and practically applied this method to real data from a coal measure reservoir in Block A of the Ordos Basin, China. The experimental results showed that our method significantly improved the learning ability of the individual learners on the small sample and performed most consistently when the hyperparameter changes. Moreover, random forest (RF) and deep NN (DNN), as the comparison methods in practical applications, were slightly inferior to ours due to greater computational effort and lower robustness and prediction accuracy. This demonstrates the superiority of our method for fast and effective log-based gas identification, and also suggests that stacking has great potential that is not limited to gas identification tasks.

Image classification of Chinese medicinal flowers based on convolutional neural network.

Traditional Chinese medicine has used many herbs on the prevention and treatment of diseases for thousands of years. However, many flowers are poisonous and only few herbs have medicinal properties. Relying on experts for herbs identification is time consuming. An efficient and fast identification method is proposed in this study. This study proposes ResNet101 models by combining SENet and ResNet101, adding convolutional block attention module or using Bayesian optimization on Chinese medicinal flower classification. The performances of the proposed ResNet101 models were compared. The best performance for accuracy, precision, recall, F1-score and PR-AUC are coming from ResNet101 model with Bayesian optimization which are 97.64%, 97.99%, 97.86%, 97.82% and 99.72%, respectively. The proposed ResNet101 model provides a better solution on the image classification of Chinese medical flowers with favourable accuracy.

A fast identification method of UAV inspection image target for substation equipment

A fast identification method of UAV inspection image target for substation equipment

A fast identification method of UAV inspection image target for substation equipment

A fast identification method of UAV inspection image target for substation equipment

Rapid Identification of Insecticide- and Herbicide-Tolerant Genetically Modified Maize Using Mid-Infrared Spectroscopy

Genetically modified (GM) technology is of great significance for increasing crop production, protecting biodiversity, and reducing environmental pollution. However, with the frequent occurrence of safety events regarding GM foods, more and more disputes have arisen over the potential safety of transgenic technology. It is particularly necessary to find a fast and accurate method for transgenic product identification. In this research, mid-infrared spectroscopy, coupled with chemometric methods, was applied to discriminate GM maize from its non-GM parent. A total of 120 GM maize and 120 non-GM maize samples were prepared, and the spectral information in the range of 400–4000 cm−1 was collected. After acquiring the spectra, wavelet transform (WT) was used to preprocess the data, and k-means was carried out to split all samples into calibration and prediction sets in the ratio of 2:1. Principal component analysis (PCA) was then conducted to qualitatively distinguish the two types of samples, and an apparent cluster was observed. Since the full spectrum covered a large amount of data and redundant information, we adopted the successive projections algorithm (SPA) to select optimal wavelengths for further analysis. Chemometrics, including partial least squares-discriminant analysis (PLS-DA), the k-nearest neighbor algorithm (KNN), and the extreme learning machine (ELM), were performed to establish classification models based on full spectra and optimal wavelengths. The overall results indicated that ELM models based on full spectra and optimal spectra showed better accuracy and reliability, with a 100% recognition rate in the calibration set and a 98.75% recognition rate in the prediction set. It has been confirmed that mid-infrared spectroscopy, combined with chemometric methods, can be a novel approach to identify transgenic maize.

AbAgIntPre: A deep learning method for predicting antibody-antigen interactions based on sequence information.

Antibody-mediated immunity is an essential part of the immune system in vertebrates. The ability to specifically bind to antigens allows antibodies to be widely used in the therapy of cancers and other critical diseases. A key step in antibody therapeutics is the experimental identification of antibody-antigen interactions, which is generally time-consuming, costly, and laborious. Although some computational methods have been proposed to screen potential antibodies, the dependence on 3D structures still limits the application of these methods. Here, we developed a deep learning-assisted prediction method (i.e., AbAgIntPre) for fast identification of antibody-antigen interactions that only relies on amino acid sequences. A Siamese-like convolutional neural network architecture was established with the amino acid composition encoding scheme for both antigens and antibodies. The generic model of AbAgIntPre achieved satisfactory performance with the Area Under Curve (AUC) of 0.82 on a high-quality generic independent test dataset. Besides, this approach also showed competitive performance on the more specific SARS-CoV dataset. We expect that AbAgIntPre can serve as an important complement to traditional experimental methods for antibody screening and effectively reduce the workload of antibody design. The web server of AbAgIntPre is freely available at http://www.zzdlab.com/AbAgIntPre.

An integrated approach utilizing raman spectroscopy and chemometrics for authentication and detection of adulteration of agarwood essential oils.

Agarwood is a precious aromatic plant which has good pharmacological effects such as antidepressant and sedation. It also has good ornamental and collection value. However, due to it is long and complex production process, the output of agarwood essential oils (AEOs) is scarce, so the price is expensive, the quality is uneven, and the adulteration events is endless. From the commercial and pharmaceutical point of view, the authenticity and quality of the commercial products labeled as AEOs is very important. This paper tested the applicability of Raman spectroscopy combined with chemometrics in classification and authenticity identification of AEOs. In this study, Raman spectroscopy and principal component analysis (PCA) combined with partial least square discriminant analysis (PLS-DA) were used to comprehensively evaluate AEOs from different geographical origins and/or extracted by different methods which showed different characteristic bands. The characteristic component of AEOs, chromone derivatives, and two commonly used adulterants were also detected. These characteristic bands provide spectrum information of AEO samples and reference materials, which can be used as Raman spectral markers for the qualitative identification of AEOs. This study can provide a novel, fast and convenient method for identification of AEOs.

A fast data-driven topology identification method for dynamic state estimation applications

This paper proposes a fast topology identification method to avoid estimation errors caused by network topology changes. The algorithm applies a deep neural network to determine the switching state of the branches that are relevant for the execution of a dynamic state estimator. The proposed technique only requires data from the phasor measurement units (PMUs) that are used by the dynamic state estimator. The proposed methodology is demonstrated working in conjunction with a frequency divider-based synchronous machine rotor speed estimator. A centralized and a decentralized approach are proposed using a modified version of the New England test system and the Institute of Electrical and Electronics Engineers (IEEE) 118-bus test system, respectively. The numerical results in both test systems show that the method demonstrate the reliability and the low computational burden of the proposed algorithm. The method achieves a satisfactory speed, the decentralized approach simplifies the training process and the algorithm proves to be robust in the face of wrong input data.

Attack Identification for Nonlinear Systems Based on Sparse Optimization

Adversarial attacks on controllers of dynamic systems have become a serious threat to many real-world systems, making methods for fast identification of attacks an indispensable part of autonomous systems. With the increasing use of model-based controllers, it is valid to exploit model knowledge also for attack identification as long as privacy of individual components is maintained. A scalable, model-based method to reveal generic attacks was introduced in our previous work and is further investigated here. It is designed for coupled systems with nonlinear dynamics and monitors certain coupling states. Based on the exchange of local sensitivity information, it approximates the propagation of an attack through the network and solves a sparse optimization problem to identify the attack. We provide a thorough derivation of the approach and analyze the involved approximation errors to prove rigorous guarantees for successful identification. In an extensive numerical case study with the IEEE 30 bus power system, we prove that not only the guarantees apply for a nonlinear, practically relevant example, but the method also identifies attacked buses with very high success rates.

Finite Element Analysis of Axial Flux Permanent Magnetic Hysteresis Dampers Based on Vector Jiles-Atherton Model

Some techniques are proposed to model axial flux permanent magnetic hysteresis damper (APHD) based on vector Jiles-Atherton (JA) model. These techniques improve calculation convergence and reduce calculation time. A fast identification method based on the numerical method and the genetic algorithm is used to obtain the parameters of the JA hysteresis model. The comparison between the measured and the calculated hysteresis curves proves that the identification method has the advantages of high accuracy and efficiency. The influence of the eddy current, dimensional parameters and the proportion of magnetization on the output torque of APHD is analyzed. A prototype is used to check the reliability of the simulation. The result shows that the 3-D FEM has high calculation accuracy.

A fast identification method of train axle crack parameters based on semi-inverse Kriging method

On-line monitoring of crack parameters can prevent axle breaking accidents and potentially disastrous consequences, while satisfying the requirement of the damage tolerance, and improving the economy in operation. A fast and reliable crack parameters identification method is required for on-line monitoring of a large number of axles. Based on the response surface characteristics of crack position and depth at two sensors, a new method is proposed adopting two Kriging surrogate models constructed by the 2xRev values of vibration signals from two measurement points. Firstly, by exchanging part of inputs and outputs, a semi-inverse Kriging model is constructed to obtain a crack equivalent line. Then, the crack equivalent line is projected on another Kriging model as the convergence path, which is the key step transforming a two-objective optimization problem into a one-dimensional searching problem. Finally, an improved dichotomy method is used to obtain the result, by which the total number of steps and the calculation time is controlled on the premise of ensuring accuracy. Different from current multi-objective optimization algorithms searching with parallel computing, the proposed method reduces the number of parameters and searching model space to be one, belonging to serial computing. The comparison to other methods shows excellent features in computing speed and stability, and the experiment was implemented on a double-disk rotor system with similar structure to a wheelset, validating the proposed methodology.

Nearest neighbor search on embeddings rapidly identifies distant protein relations.

Since 1992, all state-of-the-art methods for fast and sensitive identification of evolutionary, structural, and functional relations between proteins (also referred to as "homology detection") use sequences and sequence-profiles (PSSMs). Protein Language Models (pLMs) generalize sequences, possibly capturing the same constraints as PSSMs, e.g., through embeddings. Here, we explored how to use such embeddings for nearest neighbor searches to identify relations between protein pairs with diverged sequences (remote homology detection for levels of <20% pairwise sequence identity, PIDE). While this approach excelled for proteins with single domains, we demonstrated the current challenges applying this to multi-domain proteins and presented some ideas how to overcome existing limitations, in principle. We observed that sufficiently challenging data set separations were crucial to provide deeply relevant insights into the behavior of nearest neighbor search when applied to the protein embedding space, and made all our methods readily available for others.

CNSC-15. DECIPHERING RAMAN-SPECTRAL TISSUE HETEROGENEITY IN GLIOBLASTOMA

Abstract Raman Spectroscopy is able to provide a fast identification method for healthy and pathological tissues without the need for sample preparation. This makes it highly interesting for intraoperative use in identifying tumor types and borders. However, the capability of a machine-learning classifier to recognize known tissues based on Raman spectra relies on a solid ground truth that is usually provided by measuring small tissue samples first and analyzing the sample later by histopathology. This approach is limited in very closely interspersed tissues that cannot be identified macroscopically, such as glioblastoma where vital tumor tissue, necrosis and peritumoral tissue with characteristic changes to healthy brain tissue are tightly arranged. These areas have been shown to be distinct in their spectral properties, which makes tumor border identification aided by Raman Spectroscopy challenging. As infiltrative tumors leaving no tumor-cell-free brain tissue, borders need to be defined on a clinical scale, where extensive resection must be weighed against retaining functional brain tissue. For this, spectral analysis beyond tumor identification is necessary. In a computational approach we use methods of unsupervised learning and K-means Clustering to investigate heterogeneity within glioblastomas in the native state in order to reduce the complexity of this tumor type. Thereby, it was possible to extract spectra of necrotic tumor areas and to calculate a prediction probability for necrotic tissue within glioblastomas. Several clusters with spectral similarity to each other could be identified, which might represent the different areas of a glioblastoma. Two of these clusters were identified as probable grey and white matter, respectively, by comparison with autopsy tissue. In addition, this assignment independently confirmed the prediction of the classifier as non-necrotic tissue. Deciphering the spectral heterogeneity of glioblastoma in this way may be a great advantage for the surgeon to identify tumor borders and assist in resection control.

Evaluation of photogrammetric methods for fast identification of defects

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informujące swoich czytelników o najnowszych osiągnięciach naukowych i nowoczesnych rozwiązaniach technicznych w Polsce i na świecie, popularyzuje problemy techniczne oraz poszerza wiedzę i kulturę techniczną.

Effect of Environment on Morphometric Characteristics of Mung bean (vigna radiata (L.) Genotypes through Seed Digital Imaging Analysis

Digital imaging is a fast and reliable method for cultivar identification and discrimination. Computer seed digital imaging was utilized in this study to evaluate the differences in seed metric traits in ten genotypes of mung bean as affected by the seed production environment. A hundred seeds in each of the mung bean genotypes were subjected to digital imaging analysis using the ‘WinSEEDLE™’ software to differentiate the seed metric characters. For every replication, a hundred seeds were placed on the lighting hood in such a way that the embryo axis of the seed faces the image analysis system and the longitudinal axis runs parallel to the surface of the scanner. Seeds were automatically analyzed by the scanner and the image of the seed was recorded by the ‘WinSEEDLE™’. The procedure of hundred seeds placement on seed digital image was repeated three times for each genotype. The parameters observed were seed area, straight length, curve length, straight width, curve width, width length, and seed perimeter. Scan data collected from ‘WinSEEDLE™’ were subjected to analysis of variance and principal component analysis. The result revealed that the Mung bean genotypes evaluated were highly variable in all the seed metric traits evaluated. The study recommended that attention should be given to genotypes and seed production environments in the seed production of Mung bean. Genotypes Tvr-73, Tvr-27, Tvr-98, and Tvr-78 have been identified with consistent and high seed morphometric characteristic performance for most of the attributes examined, hence, can be important criteria in selecting superior seed physical traits and could be used as parental material, in the development of high seed yielding varieties.

Detection of Three-Phase Voltage Sag under Multiple Power Quality Disturbances

Voltage sag is an unavoidable short-term power quality disturbance problem in the power system. Rapid and accurate detection of voltage sag characteristic quantities is the premise of dealing with the sag problem. Based on the principle of d-q transformation and ISOGI-QSG structure, this paper proposes a method for fast detection and identification of three-phase voltage sag. It describes the working principle of the improved SOGI in detail. In addition, the structure of three-phase voltage sag independent detection is analyzed, and a fast determination method for sag fault types is proposed. The advantages of this method compared with traditional three-phase voltage detection are verified by simulation comparison, and it still has good anti-interference ability and better dynamic performance under various power quality disturbances. Moreover, the detection speed can meet the time requirements of sensitive loads for dual power switching.

Biomedical and Biochemical Approaches and Strategies for Targeting and Delivery of Cadmium Oxide (CdO) Nanoparticles Aggregation Linked to DNA/RNA by Aryl Mercaptanes with Various Chain Length

CdO nanoparticles show a strong peak of Plasmon absorption in ultraviolet-visible zone. A strong interaction exists between the surface of CdO nanoparticles and aryl mercaptan compounds. Aryl mercaptan compounds cause to aggregation of CdO nanoparticles linked to DNA/RNA and hence, lead to widening of peak Plasmon of CdO nanoparticles surface at 550 (nm) and emerging a new peak at higher wavelength. In the current project, this optical characteristic of CdO nanoparticles is used to time investigate of interaction between different aryl mercaptanes and CdO nanoparticles. The results were shown that aryl mercaptan compounds with shorter chain length interact faster with CdO nanoparticles. Therefore, a simple and fast method for identification of aryl mercaptanes with various chain length using red shift in surficial Plasmon absorption is presented.