Pattern Recognition Methods Research Articles

Comparison of Accuracy between Random Forest Method and K-Nearest Neighbors Method for Recognizing Solar Panel Energy Conversion Temperature

This study analyzes the classification of temperature image pattern recognition on solar panels to improve the accuracy of energy conversion performance affected by weather changes. The process begins by capturing the surface temperature image of the panel as primary data, which is then processed through the pattern recognition stage. The pattern recognition method is chosen to detect and understand patterns in temperature images that will be used as datasets. This study also compares the results of pattern recognition using the Random Forest classification method with the K-Nearest Neighbors (KNN) method, in order to build an effective model in analyzing images based on weather temperature in Medan City. The results of the study obtained that the solar panel produced a maximum output of 15.74 Wp at 12:00 pm, when the temperature tends to be higher and sunlight is optimal. Then the results of the random forest method showed good performance with an accuracy of 84% and the K-Nearest Neighbors method had an accuracy of 78%.

Application of Pattern Recognition Methods to Study Spatial Localization of Polymetallic Mineralization in the Altai–Sayan Region

The Altai–Sayan mountain-folded belt is analyzed with the purpose of (1) revealing peculiarities of localization of large-scale polymetallic mineralization in the lineament-block structure of the region and (2) determining the geophysical and geomorphic peculiarities of the locations of these deposits using the Cora-3 pattern recognition algorithm. The lineament-block structure of the region is determined using morphostructural zoning. A spatial correlation between large and superlarge polymetallic deposits and morphostructural nodes is revealed. Based on this correlation, a dichotomy problem is solved, which is to divide the entire set of nodes in the region into two classes—ore-bearing and non-ore bearing. For this purpose, we used the Cora-3 logical recognition algorithm with training, for which the input data are geomorphological and geophysical parameters of the nodes. The training set of the algorithm was composed of the nodes where large and superlarge polymetal deposits are known. At the training stage, the algorithm identified the sets of the characteristic features that are peculiar to each class. Based on these features, all the nodes in the region were divided into ore-bearing and non-ore-bearing ones. As a result of recognition, the nodes in which deposits of the considered types and sizes are known were classified as ore-bearing, and, in addition to them, another 11 nodes were identified that meet the features determined in the work and can be considered potentially ore-bearing.

An expert system for species discrimination and grade identification of fish maw

Fish maw, derived from dried swim bladders of fish, is valued for its nutritional and medicinal properties, which has led to an increased market demand. However, price variability based on species and grades has results in unethical practices such as counterfeiting and mislabeling, highlighting the need for reliable quality authentication. To address this, an expert system using MATLAB software has been developed. This system employs nuclear magnetic resonance (NMR) technology and pattern recognition methods to identify fish maw species and classify their grade. The study analyzed ten species across three grades of fish maw, identifying 43 nutritional components from NMR spectra, including sugars, amino acids, fatty acids, organic acids, and vitamins. Univariate statistical analysis was integrated with multivariate statistical analyses, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares (OPLS-DA) using SIMCA software, to develop models for species and grades identification. A four-dimensional volcano map was constructed to highlight characteristic components of various fish maws, resulting in an NMR database for common fish maws. The expert system’s accuracy was validated with new samples, achieving 92.6% for species identification and 90.0% for grade classification. This study provides a valuable tool for the quality evaluation of fish maw and a scientific basis for market regulation.

LEVERAGING EPILEPSY DETECTION ACCURACY THROUGH BURST ENERGY INTEGRATION IN CWT AND DECISION TREE CLASSIFICATION OF EEG SIGNALS

The biomedical field plays a pivotal role in advancing healthcare by leveraging technological innovations to enhance diagnostics and treatment strategies. In the context of neurological disorders, particularly epilepsy, automated EEG signal processing stands out as a critical facet of biomedical research. The ability to analyze and interpret vast amounts of electroencephalogram (EEG) data using sophisticated techniques, such as Continuous Wavelet Transform (CWT), contributes significantly to the timely and accurate detection of epileptic events. Automated EEG signal processing, which uses advanced algorithms and pattern recognition methods, enables the identification of subtle yet crucial patterns indicative of epileptic activity. This paper presents an in-depth exploration of epilepsy identification using the CWT on the CHB-MIT Scalp EEG database. The study uses nine complex mother wavelets from the Gaussian and Morlet families to look at 8920 EEG segments, including 197 seizure events. The performance of each wavelet in detecting epileptic convulsions within EEG signals is rigorously evaluated. Our study shows that the complex Gaussian wavelet of order 5 (cgau5) emerged as the optimal choice, with a sensitivity of 97.58% and a precision of 97.93%. To improve epilepsy detection, we introduce burst energy, a novel engineered feature. This method gets accurate information about brain activity from the CWT scalogram by detecting ictal and ictal-free EEGs at different energy levels. The use of burst energy has a significant impact on classification performance, highlighting its potential for improved accuracy in epilepsy identification. This comprehensive study contributes valuable insights into selecting appropriate wavelets and introduces an innovative feature for more effective EEG-based epilepsy detection.

A novel aggregation-induced emission-featured hyperbranched poly(amido amine)s stabilized copper nanoclusters‑cerium (III) sensor for detection of thiol flavor compounds in processed meat

Thiol flavor compounds are a class of flavoring ingredients that contribute significantly to food flavor. However, rapid discrimination of multiple thiol-flavor compounds remain a challenge. In this study, a ratiometric fluorescent sensor (TPE-ssHPA@Cu NCs-Ce3+) with dual-channel fluorescence features was developed using tetraphenylethene-embedded hyperbranched poly(amidoamine) as a template to stabilize the copper nanocluster‑cerium ions. The sensor was explored for the specific discrimination of six typical thiol flavor compounds, each producing diverse fluorescent fingerprints that were further identified using pattern recognition methods. The sensor achieved a rapid response in identifying thiol flavor compounds and multicomponent mixtures, with detection limits of 0.32–3.13 μM. Furthermore, it was successfully applied to differentiate between the different types and cooking times of meat broths.

A systematic review on diagnosis methods for rolling bearing compound fault: research status, challenges, and future prospects

Abstract Rolling Bearing Compound Fault (RBCF) is characterized by randomness, sequentiality, coupling, and concealment, which is one of the primary causes for unscheduled downtime of rotating machinery. Therefore, timely detecting defects is essential to reduce downtime and ensure safety of equipment. This paper provides a systematic review for the existing applications and developments of diagnosis methods of RBCF since 2004. Categorized as fault mechanism analysis methods based on analytical model, feature extraction methods based on signal processing, and pattern recognition methods based on artificial intelligence, and their diagnostic frameworks are summarized in detail, respectively. The advantages and disadvantages of the reviewed methods are concluded. The challenges and prospects for the diagnosis methods of RBCF are analyzed and discussed in further. This work can offer valuable insights and research inspiration for academic scholars and industry engineers in diagnosing compound faults for rolling bearing.

A Convolutional Neural Network-Based Method for Distinguishing the Flow Patterns of Gas-Liquid Two-Phase Flow in the Annulus

In order to improve the accuracy and efficiency of flow pattern recognition and to solve the problem of the real-time monitoring of flow patterns, which is difficult to achieve with traditional visual recognition methods, this study introduced a flow pattern recognition method based on a convolutional neural network (CNN), which can recognize the flow pattern under different pressure and flow conditions. Firstly, the complex gas–liquid distribution and its velocity field in the annulus were investigated using a computational fluid dynamics (CFDs) simulation, and the gas–liquid distribution and velocity vectors in the annulus were obtained to clarify the complexity of the flow patterns in the annulus. Subsequently, a sequence model containing three convolutional layers and two fully connected layers was developed, which employed a CNN architecture, and the model was compiled using the Adam optimizer and the sparse classification cross entropy as a loss function. A total of 450 images of different flow patterns were utilized for training, and the trained model recognized slug and annular flows with probabilities of 0.93 and 0.99, respectively, confirming the high accuracy of the model in recognizing annulus flow patterns, and providing an effective method for flow pattern recognition.

Feature Fusion of Pulse Current, Ultrahigh Frequency, and Photon Count Signal: A Novel Discharge Pattern Recognition Method of Metal Particles in GIS/GIL

Feature Fusion of Pulse Current, Ultrahigh Frequency, and Photon Count Signal: A Novel Discharge Pattern Recognition Method of Metal Particles in GIS/GIL

Progress in research of multimorbidity measurement and analysis methods

Multimorbidity is significantly associated with life quality decline, disability, and increased mortality risk. Additionally, it leads to greater consumption of healthcare resources, presenting substantial challenges to healthcare systems globally. To better assess the burden of multimorbidity, its impact on patient health outcomes and healthcare services, and to explore the underlying mechanisms in its development, this paper summarizes the existing methods used for measuring and analyzing multimorbidity in research and practice, including disease count, disease-weighted indices, multimorbidity pattern recognition (such as disease association analysis, clustering analysis, and network analysis) and longitudinal methods to provide references for the accurate assessment of the prevalence of multimorbidity and its changes and improve the validity and universality of research findings.

Advances in Gas Detection of Pattern Recognition Algorithms for Chemiresistive Gas Sensor.

Gas detection and monitoring are critical to protect human health and safeguard the environment and ecosystems. Chemiresistive sensors are widely used in gas monitoring due to their ease of fabrication, high customizability, mechanical flexibility, and fast response time. However, with the rapid development of industrialization and technology, the main challenges faced by chemiresistive gas sensors are poor selectivity and insufficient anti-interference stability in complex application environments. In order to overcome these shortcomings of chemiresistive gas sensors, the pattern recognition method is emerging and is having a great impact in the field of sensing. In this review, we focus systematically on the advancements in the field of data processing methods for feature extraction, such as the methods of determining the characteristics of the original response curve, the curve fitting parameters, and the transform domain. Additionally, we emphasized the developments of traditional recognition algorithms and neural network algorithm in gas discrimination and analyzed the advantages through an extensive literature review. Lastly, we summarized the research on chemiresistive gas sensors and provided prospects for future development.

Fast identification of Baijius based on organic acid response colorimetric sensor array

Herein, a simple colorimetric sensor array sensitive to organic acids was designed by gold (Au) and silver (Ag) nanoparticles modified with four different compounds for the identification of 16 famous Baijius containing different organic acids. In detail, the sensing mechanism of the colorimetric sensor array was based on the charge transfer between the nanoparticle surface protectant and H+, which reduced the electrostatic force between the nanoparticles, leading to the aggregation and color change of the nanoparticles. The unique color change of the colorimetric array before and after the reaction was used as a unique fingerprint profile for each specific analyte, which can be identified by the naked eye. The generated digital database was analyzed using pattern recognition methods, including principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). All Baijius can be easily identified by fingerprint mapping or PCA scoring maps. 32 blind samples were tested by LDA and 30 samples were correctly classified. The HCA results showed that all samples were correctly classified and even very similar 1 % dilutions of the Baijiu can be easily distinguished, demonstrating the potential of the constructed colorimetric sensor array technology for quality control applications of Baijius and other beverages.

Literature Review on Multi-Criteria Analysis and Application in Education Environment

Due to the increasing complexity of educational data, the use of decision analysis techniques such as Multi-Criteria Decision Making (MCDM) models has become more popular in the education system in recent years. Multi criteria decision making methods provide data analysis methods faster and more efficient for revealing unhidden patterns and other meaningful information from vast educational data those conventional analytics are unable to discover in a reasonable amount of time. Particularly, MCDM techniques have been demonstrated to be effective methods for pattern recognition in educational systems. Motivated by this consideration, the purpose of this paper is to investigate the MCDM approaches applied to education systems through a review of new architectures, applications, and educational trends. The primary objective of this paper is to provide extensive insight into the application of MCDM models to education solutions in order to bridge the gap between MCDM techniques and human-based education interpretability. Then, the application of MCDM to various aspects of education are categorized. Finally, we present the current open challenges and future directions.

Multivariate Modelling Based on Isotopic, Elemental, and Fatty Acid Profiles to Distinguish the Backyard and Barn Eggs.

The ability to trace the origin of eggs from backyard-raised hens is important due to their higher market value compared to barn-raised eggs. This study aimed to differentiate eggs from these two rearing systems using isotopic, elemental, and fatty acid profiles of egg yolks. A total of 90 egg yolk samples were analyzed, analytical results being followed by statistical tests (Student's t-test) showing significant differences in δ18O, several elements (Mg, K, Sc, Mn, Fe, Ni, Cu, Zn, As, Cd, Ba, Pb), and fatty acids compositions (C23:0, C17:0, C18:0, C16:1n7, C18:1n9, C18:2n6, C20:1n7, C20:4n6, C20:5n3, C22:6n3), as well as in the ratios of SFA, PUFA, and UFA. The results indicated a nutritional advantage in backyard eggs due to their lower n-6 polyunsaturated fatty acid content and a more favorable n-6 to n-3 ratio, linked to differences in the hens' diet and rearing systems. To classify the production system (backyard vs. barn), three pattern recognition methods were applied: linear discriminant analysis (LDA), k-nearest neighbor (k-NN), and multilayer perceptron artificial neural networks (MLP-ANN). LDA provided perfect initial separation, achieving 98.9% accuracy in cross-validation. k-NN yielded classification rates of 98.4% for the training set and 85.7% for the test set, while MLP-ANN achieved 100% accuracy in training and 92.3% in testing, with minor misclassification. These results demonstrate the effectiveness of fusion among isotopic, elemental, and fatty acid profiles in distinguishing backyard eggs from barn eggs and highlight the nutritional benefits of the backyard-rearing system.

Identifying High-Risk Patterns in Single-Vehicle, Single-Occupant Road Traffic Accidents: A Novel Pattern Recognition Approach

Despite various interventions in road safety work, fatal and severe road traffic accidents (RTAs) remain a significant challenge, leading to human suffering and economic costs. Understanding the multicausal nature of RTAs, where multiple conditions and factors interact, is crucial for developing effective prevention measures in road safety work. This study investigates the multivariate statistical analysis of co-occurring conditions in RTAs, focusing on single-vehicle accidents with single occupancy and personal injury on Austrian roads outside built-up areas from 2012 to 2019. The aim is to detect recurring combinations of accident-related variables, referred to as blackpatterns (BPs), using the Austrian RTA database. This study proposes Fisher’s exact test to estimate the relationship between an accident-related variable and fatal and severe RTAs (severe casualties). In terms of pattern recognition, this study develops the maximum combination value (MCV) of accident-related variables, a procedure to search through all possible combinations of variables to find the one that has the highest frequency. The accident investigation proceeds with the application of pattern recognition methods, including binomial logistic regression and a newly developed method, the PATTERMAX method, created to accurately detect and analyse variable-specific BPs in RTA data. Findings indicate significant BPs contributing to severe accidents. The combination of binomial logistic regression and the PATTERMAX method appears to be a promising approach to investigate severe accidents, providing both insights into detailed variable combinations and their impact on accident severity.

Application of Pattern Recognition Methods to Study Spatial Localization of Polymetallic Mineralization in the Altai–Sayan Region

Application of Pattern Recognition Methods to Study Spatial Localization of Polymetallic Mineralization in the Altai–Sayan Region

Luminescent Metal-Organic Framework-Based Fluorescent Sensor Array for Screening and Discrimination of Bisphenols.

Extensive applications of bisphenols in industrial products have led to their release into aquatic environments, causing a great threat to human health due to their endocrine-disrupting effects, whereas existing methods are difficult to implement the rapid and high-throughput detection of multiple bisphenols. To circumvent this issue, we constructed a sensor array using two luminescent metal-organic frameworks (LMOFs) (Zr-BUT-12 and Ga-MIL-61) for the rapid discrimination of six bisphenol contaminants (BPA, BPS, BPB, BPF, BPAF, and TBBPA). Wherein, Zr-BUT-12 and Ga-MIL-61 exhibited different fluorescence-emission properties and good luminescent stability. Interestingly, bisphenols with different structures had diverse quenching effects on the fluorescence intensity of Zr-BUT-12 and Ga-MIL-61 via the adsorptive interaction, resulting in unique fluorescent fingerprints. Based on pattern recognition methods, different bisphenols were successfully identified, with the limit of detection in the range of 1.59-16.7 ng/mL for six bisphenols. More importantly, the developed sensor array could be effectively utilized for distinguishing different ratios of mixed bisphenols, which was further applied for bisphenol discrimination in real water samples. Consequently, our finding provides a promising strategy for the simultaneous recognition of multiple bisphenols, which encourages the development of a sensor array for the detection of multiple contaminants in environmental monitoring and food safety.

Production monitoring and quality characterization of black garlic using Vis-NIR hyperspectral imaging integrated with chemometrics strategies

As a new deep-processing garlic product with notable health benefits, the accurate discrimination of processing stages and prediction of key physicochemical constituents in black garlic are vital for maintaining product quality. This study proposed a novel method utilizing hyperspectral imaging technology to both rapidly monitor the processing stages and quantitatively predict changes in the key physicochemical constituents during black garlic processing. Multiple methods of noise reduction and feature screening were used to process the acquired hyperspectral information. To differentiate processing stages, pattern recognition methods including linear discriminant analysis (LDA), K-nearest neighbor (KNN), support vector machine classification (SVC) analysis were utilized, achieving a discriminant accuracy of up to 98.46 %. Furthermore, partial least squares regression (PLSR) and support vector machine regression (SVR) analysis were performed to achieve quantitative prediction of the key physicochemical constituents including moisture and 5-HMF. PLSR models outperformed SVR models, with correlation coefficient of prediction of 0.9762 and 0.9744 for moisture and 5-HMF content, respectively. The current study can not only offer an effective approach for quality detection and assessment during black garlic processing, but also have a positive significance for the advancement of black garlic related industries.

Optimal weighted multi-scale entropy-energy ratio feature for machine fault diagnosis

Identifying the sensitive characteristics of mechanical equipment components is crucial for effective fault diagnosis. However, focusing solely on a specific feature at a single time scale fails to comprehensively capture the device’s operational state. Inspired by the concept of multi-scale analysis and recognizing the complementary strengths of permutation entropy (PE) and root mean square (RMS) in fault characterization, we propose a novel feature called the Optimal Weighted Multi-Scale Entropy-Energy Ratio (OWMEER). This feature aims to enhance fault characterization by optimally combining the strengths of PE and RMS, thereby providing a more comprehensive assessment of the device’s condition. The effectiveness and superiority of OWMEER in fault characterization have been validated through experimental data, including both public and self-test datasets, when combined with the commonly used pattern recognition methods such as random forest (RF) and support vector machine (SVM). The results demonstrate that using OWMEER as a fault feature not only yields better results than using the original features RMS and PE, but also maintains strong diagnostic performance across different classifiers and datasets.

A one-dimensional higher-order dynamic modeling method for thin-walled beams with circular cross-sections

This paper addresses the construction of a dynamical model for a thin-walled beam with circular cross-section in the framework of one-dimensional higher-order beam theory. And a method for pattern recognition of circular thin-walled structures is proposed based on principal component analysis. Initially, a set of equal length linear segments are defined to discretize the mid-line of a circular section. Preliminary deformation modes of thin-walled structures, defined over the cross-section through kinematic concept, are parametrically derived through changing the discretization degree of the section. Next, the generalized eigenvectors are derived from the governing equations, and the characteristic deformation modes of circular sections with different discretization degrees are solved based on principal component analysis. In addition, a reduced higher-order model can be obtained by updating the initial governing equations with a selective set of cross-section deformation modes. The features include further reducing the number of degree of freedoms (DOFs) and significantly improving computational efficiency while ensuring accuracy. For illustrative purposes, the versatility of the procedure is validated through both numerical examples and comparisons with other theories.

Huangqi Guizhi Wuwu Decoction Improves Inflammatory Factor Levels in Chemotherapy-induced Peripheral Neuropathy by Regulating the Arachidonic Acid Metabolic Pathway.

Chemotherapy-induced Peripheral Neuropathy (CIPN) is a common complication that arises from the use of anticancer drugs. Huangqi Guizhi Wuwu Decoction (HGWWD) is an effective classic prescription for treating CIPN; however, the mechanism of the activity is not entirely understood. This study aimed to investigate the remedial effects and mechanisms of HGWWD on CIPN. Changes in behavioral, biochemical, histopathological, and biomarker indices were used to evaluate the efficacy of HGWWD treatment. Ultra-high-performance liquid chromatography/mass spectrometry combined with the pattern recognition method was used to screen biomarkers and metabolic pathways related to CIPN. The results of pathway analyses were verified by protein blotting experiments. A total of 29 potential biomarkers were identified and 13 metabolic pathways were found to be involved in CIPN. In addition HGWWD reversed the levels of 19 biomarkers. Prostaglandin H2 and 17α,21-dihydroxypregnenolone were targeted as core biomarkers. This study provides scientific evidence to support the finding that HGWWD mainly inhibits the inflammatory response during CIPN by regulating arachidonic acid metabolism.