A comprehensive review of data processing and target recognition methods for ground penetrating radar underground pipeline B-scan data by Chen Liu, Jue Li, Zhengnan Liu, Sirui Tao, Mingxuan Li

“…The unique features and characteristics of GPR pipeline B-scan data were initially examined, including the impact of pipeline materials, scanning methods, and electromagnetic wave frequencies. Traditional signal processing techniques, such as filtering, wavelet transform, and empirical mode decomposition, as well as emerging machine learning and deep learning-based methods for denoising, feature extraction, and target recognition, were systematically reviewed. …”
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NORMALISATION OF THE DRIVE PRECISION OF METAL-CUTTING MACHINES by Irina A. Klenova, Dmitry A. Rudikov, Svetlana N. Kholodova

“…In order to determine and evaluate drive errors in metal-cutting machines, three methods were proposed: direct measurement of the frequencies of the series, calculations using kinematic balance equations and summation of individual components. …”
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Fault diagnosis of nonlinear analog circuits using generalized frequency response function and LSSVM. by Jialiang Zhang, Yaowang Yang

“…A fault diagnosis method of nonlinear analog circuits is proposed that combines the generalized frequency response function (GFRF) and the simplified least squares support vector machine (LSSVM). …”
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Calculating Rock Joint Frequency in TBM Excavation Through Binocular Vision and Segmentation Techniques by Jingwei Xu, Haochen Sun, Hongmei Wang, Yaxu Wang, Yan Zhu, Peng Jiang

“…The method enhances the speed and precision of determining rock mass integrity parameters, specifically the frequency of rock joints, thereby providing reliable and efficient rock mechanics data for TBM operations. …”
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A New Method of Intelligent Fault Diagnosis of Ship Dual-Fuel Engine Based on Instantaneous Rotational Speed by Ji Gan, Huabiao Jin, Qianming Shang, Chenxing Sheng

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Continuous robust sound event classification using time-frequency features and deep learning. by Ian McLoughlin, Haomin Zhang, Zhipeng Xie, Yan Song, Wei Xiao, Huy Phan

“…Recent advances in this field have been achieved by machine learning classifiers working in conjunction with time-frequency feature representations. …”
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A literature review: AI models for road safety for prediction of crash frequency and severity by Muneeb Shehzad Butt, Muhammad Awais Shafique

“…Abstract Artificial intelligence and machine learning have brought a new paradigm in road safety, moving from the traditional approach to adopting data-driven techniques for predicting the frequency and severity of crashes. …”
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Ball Screw Fault Detection and Location Based on Outlier and Instantaneous Rotational Frequency Estimation by Liang Guo, Yingqi Huang, Hongli Gao, Li Zhang

“…In the second step, a parameterized time-frequency analysis method is utilized to extract the instantaneous rotational frequency of the ball screw system. …”
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Leveraging Variable Frequency Drive Data for Nondestructive Testing and Predictive Maintenance in Industrial Systems by Carl Lee Tolbert

“…However, traditional methods typically rely on external sensors, which can lead to increased costs and added complexity. …”
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Feature Frequency Extraction Based on Principal Component Analysis and Its Application in Axis Orbit by Zhen Li, Weiguang Li, Xuezhi Zhao

“…In this paper, the relationship between effective eigenvalues and frequency components was investigated, and a new characteristic frequency separation method based on PCA (CFSM-PCA) was proposed. …”
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Research on the Frequency Stability Analysis of Grid-Connected Double-Fed Induction Generator Systems by Peng Jia, Yun Sun, Linlin Yu, Jing Zhang, Xiaoliang Jiang, Gaojun Meng

“…The research results indicate that the proposed method can accurately quantify the impact of wind power variation on system frequency stability and rapidly determine the maximum wind power penetration rate to ensure frequency stability, thereby improving the accuracy of the wind power grid connection capability assessment.…”
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Pistachio Classification Based on Acoustic Systems and Machine Learning by Yavuz Türkay, Zekiye Seyma Tamay

“…This system performs feature extraction using Mel frequency cepstral coefficients (MFCC) and classification using support vector machine (SVM). …”
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DETAILS’ REPAIR OF CONSTRUCTION AND ROAD MACHINES: FLUCTUATIONS’ MODELLING by V. E. Ovsyannikov, V. I. Vasilyev

“…The paper studies the possibility of the calculation method’s usage in oscillatory processes, which allows assigning the cutting modes by providing required output parameters.Materials and methods. …”
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Fault Diagnosis of Oil Pumping Machine Retarder Based on Sound Texture-Vibration Entropy Characteristics and Gray Wolf Optimization-Support Vector Machine by Shutao Zhao, Ke Chang, Erxu Wang, Bo Li, Kedeng Wang, Qingquan Wu

“…The results showed that the GWO-SVM fault diagnosis method, which is based on the combination of sound texture and vibration entropy characteristics, makes full use of the complementary advantages of signal frequency band. …”
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Determination Modal parameters a Turning machining system by Юрій Петраков, Олександр Охріменко, О. Пасічник, А. Петришин

“…The cutting process model represents the components of the cutting forces acting along the coordinate axes, which allows it to be integrated into the structure of the machining system. A method for experimental modal analysis of the machining system of a lathe using a hammer, accelerometer and a storage two-channel oscilloscope has been developed. …”
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Complying with the EU AI Act: Innovations in explainable and user-centric hand gesture recognition by Sarah Seifi, Tobias Sukianto, Cecilia Carbonelli, Lorenzo Servadei, Robert Wille

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Workpiece position optimisation in robotic multi-axis machining by Tomáš Kratěna, Petr Vavruška, Jiří Švéda, Pavel Zeman

“…The disadvantages are low static stiffness and the risk that the robot structure will emit low-frequency vibrations during the machining operation. …”
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Forecasting renewable energy for microgrids using machine learning by Piyumi Sudasinghe, Damayanthi Herath, Isiwara Karunarathne, Hansani Weeratunge, Lahiru Jayasuriya

“…Results show that the 1-D CNN model achieves an improvement of up to 229.8 times in MSE and a 24.47 fold improvement in MAE compared to baseline models that use traditional statistical methods in forecasting. This demonstrates the potential of machine learning for enhancing microgrid management, particularly in short-term forecasting of renewable generation.…”
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Predictive machine health monitoring using deep convolution neural network for noisy vibration signal of rotating machine using empirical mode decomposition by R. Pavithra, Prakash Ramachandran

“…An ablation study shows that the proposed method is highly susceptible to impulse noise as well. …”
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Comparative Analysis of Machine Learning Algorithms for Antenna Alignments by Mohammad Al Bataineh, Dana I. Abu Abdoun, Mahmoud Al Ahmad

“…By providing a robust machine learning framework, this research contributes significantly to advancing automated alignment processes, reducing dependency on manual methods, and paving the way for future innovations in RF systems. …”
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