Analysis of Vibration Characteristics of the Grading Belt in Wolfberry Sorting Machines by Yang Yu, Zhiwei Su, Junhao Zhang, Jinglong Li, Wu Qin

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Prediction of Parkinson Disease Using Long-Term, Short-Term Acoustic Features Based on Machine Learning by Mehdi Rashidi, Serena Arima, Andrea Claudio Stetco, Chiara Coppola, Debora Musarò, Marco Greco, Marina Damato, Filomena My, Angela Lupo, Marta Lorenzo, Antonio Danieli, Giuseppe Maruccio, Alberto Argentiero, Andrea Buccoliero, Marcello Dorian Donzella, Michele Maffia

“…A dataset comprising 81 voice samples (41 from healthy individuals and 40 from PD patients) was utilized to train and evaluate common machine learning (ML) models using various types of features, including long-term (jitter, shimmer, and cepstral peak prominence (CPP)), short-term features (Mel-frequency cepstral coefficient (MFCC)), and non-standard measurements (pitch period entropy (PPE) and recurrence period density entropy (RPDE)). …”
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Grouped machine learning methods for predicting rock mass parameters in a tunnel boring machine‐driven tunnel based on fuzzy C‐means clustering by Ruirui Wang, Yaodong Ni, Lingli Zhang, Boyang Gao

“…Based on fuzzy C‐means clustering, this paper proposes a grouped machine learning method for predicting rock mass parameters. …”
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Time-domain virtual tapping machine method for numerical evaluation of transient acoustic performance in cruise cabins by Yingying Zuo, Deqing Yang, Jian Xiao

“…In this paper, a general time-domain virtual tapping machine method for numerical evaluation of transient acoustic performance in cruise cabins is proposed. …”
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High frequency resonance mitigation of microgrid-connected PV units using novel adaptive control based on virtual impedance and machine learning algorithm by Mohammad Hossein Nemati, Mohammad Hossein Shaabani, Navid Dehghan, Gevork B. Gharehpetian

“…This study proposes a novel adaptive control framework that combines virtual impedance (VI) methods with a machine learning-based tuning strategy using K-Nearest Neighbors (KNN) algorithm. …”
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Identification of Elephant Rumbles in Seismic Infrasonic Signals Using Spectrogram-Based Machine Learning by Janitha Vidunath, Chamath Shamal, Ravindu Hiroshan, Udani Gamlath, Chamira U. S. Edussooriya, Sudath R. Munasinghe

“…It is experimentally found that the combination of the Mel-frequency cepstral coefficient (MFCC) feature extraction method and the ridge classifier machine learning algorithm give the highest accuracy of 97% in detecting infrasonic elephant rumbles hidden in seismic signals. …”
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Fault Diagnosis of a Hydraulic Pump Based on the CEEMD-STFT Time-Frequency Entropy Method and Multiclass SVM Classifier by Wanlin Zhao, Zili Wang, Jian Ma, Lianfeng Li

“…Second, the time-frequency analysis methods, which include the Short-Time Fourier Transform (STFT) and time-frequency entropy calculation, are applied to realize the robust feature extraction. …”
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A Decomposition-Integration Framework of Carbon Price Forecasting Based on Econometrics and Machine Learning Methods by Zhehao Huang, Benhuan Nie, Yuqiao Lan, Changhong Zhang

“…First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is employed to decompose carbon price data into distinct modal components, each defined by specific frequency characteristics. …”
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MODAL ANALYSIS OF CARRIER SYSTEM FOR HEAVY HORIZONTAL MULTIFUNCTION MACHINING CENTER BY FINITE ELEMENT METHOD by Yu. V. Vasilevich, S. S. Dovnar, I. I. Shumsky

“…Virtual and operational trials of the machine have been carried out simultaneously. Modeling has been executed with the help of a finite element method (FEM). …”
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Shock Signal Trend Term Error Correction Method Based on Discrete Wavelet Transform and Low-Frequency Oscillator Combination by Peng Wang, Ming Yan, Lei Zhang, Ning Yang

“…A discrete wavelet transform (DWT) and low-frequency oscillator combination method is proposed for correcting shock signals in this paper. …”
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Micro-electrical Discharge Machining of Micro-holes Based on Integrated Orthogonal Experiments and CNN Methods by Yuandong MO, Yazhi WANG, Shuqi HUANG, Jiajun ZHONG

“…For the taper angle in micro-hole machining, the hierarchy is feed rate, pulse duty cycle, spindle speed, and pulse frequency, and the optimal combination parameters is a feed rate of 0.05 mm/s, a spindle speed of 1 500 r/min, a pulse duty cycle of 60%, and a pulse frequency of 3 000 Hz. …”
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Time–Frequency-Domain Fusion Cross-Attention Fault Diagnosis Method Based on Dynamic Modeling of Bearing Rotor System by Shiyu Xing, Zinan Wang, Rui Zhao, Xirui Guo, Aoxiang Liu, Wenfeng Liang

“…However, methods like self-attention have limitations. They only capture features within a single sequence. …”
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Dynamic Performance Analysis of High-Frequency Signal Injection Based Sensorless Methods for Interior Permanent Magnet Synchronous Motors by Ahmadreza Alaei, Dong Hee Lee, Jin woo Ahn, Sayed Morteza Saghaeian Nejad

“…Some efforts have been performed to compare such high frequency signal injection based methods from viewpoint of some parameters such as injection frequency, voltage magnitude and so on. …”
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Functional Connectivity Metrics in Temporal Lobe Epilepsy: A Machine Learning Perspective With MEG by M. V. Suhas, N. Mariyappa, A. Karunakar Kotegar, M. Ravindranadh Chowdary, K. Raghavendra, Ajay Asranna, L. G. Viswanathan, H. Anitha, Sanjib Sinha

“…Various machine learning models demonstrate high classification performance, with accuracies reaching up to 100% in particular frequency bands in agreement with the Chi2 feature importance analysis. …”
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Channel-Wise Characterization of High Frequency Oscillations for Automated Identification of the Seizure Onset Zone by Dakun Lai, Xinyue Zhang, Wenjing Chen, Heng Zhang, Tongzhou Kang, Han Yuan, Lei Ding

“…High frequency oscillations (HFOs) in intracranial electroencephalography (iEEG) recordings are a promising clinical biomarker that can help define the epileptogenic regions in the brain. …”
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Fault Diagnosis Method of Bearing based on LCD Cross Approximate Entropy and Relevance Vector Machine by Tan Jingjing, Gao Feng, Zhang Qiantu

“…Aiming at the fault diagnosis problem of rolling bearing,a fault diagnosis method of rolling bearing based on local characteristic-scale decomposition（LCD） cross approximate entropy（CAE） and relevance vector machine（RVM） is proposed. …”
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Diagnosis of Stator Inter-Turn Short Circuit Faults in Synchronous Machines Based on SFRA and MTST by Junsheng Ding, Zhongyong Zhao

“…Finally, the performance of the proposed method is tested and verified. It concludes that the proposed method has the potential for classifying and diagnosing the inter-turn short circuit of stators in synchronous machines.…”
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Optimizing the primary stability of dental implants in type IV bone: in-vitro comparison of machine-driven and ratcheting insertion protocols by Nuri Mert Tayşi, Ayşegül Erten Tayşi, Pınar Erçal, Soner Şişmanoğlu

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Intelligent Diagnosis Method for Centrifugal Pump System Using Vibration Signal and Support Vector Machine by Hongtao Xue, Zhongxing Li, Huaqing Wang, Peng Chen

“…This paper proposed an intelligent diagnosis method for a centrifugal pump system using statistic filter, support vector machine (SVM), possibility theory, and Dempster-Shafer theory (DST) on the basis of the vibration signals, to diagnose frequent faults in the centrifugal pump at an early stage, such as cavitation, impeller unbalance, and shaft misalignment. …”
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Research on CNC Machine Tool Spindle Fault Diagnosis Method Based on DRSN–GCE Model by Xiaoxu Li, Jiahao Wang, Jianqiang Wang, Jixuan Wang, Jiaming Chen, Xuelian Yu

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