Capturing High-Frequency Harmonic Signatures for NILM: Building a Dataset for Load Disaggregation by Farid Dinar, Sébastien Paris, Éric Busvelle

“…Ultimately, the dataset can be used to validate NILM, and we show through the use of machine learning techniques that high-frequency features improve disaggregation accuracy when compared with traditional methods. …”
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Projection and assessment of future droughts in Iowa: developing a machine learning model and an interactive application by Ingrid Cintura, Antonio Arenas

“…Climate change has intensified the frequency and severity of droughts, significantly impacting water resources, agriculture, and ecosystems. …”
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Implementation of Adaptive Short Time Fourier Transform and Sigmoid based Kernel Support Vector Machine for Radar Signal Identification by AHMAD Ashraf Adam, MUHAMMAD Farouk Isah

“…This paper proposes a novel approach that merges two powerful techniques: Adaptive Short-Time Fourier Transform (ASTFT) and Sigmoid Kernel Support Vector Machine (SVM). ASTFT offers exceptional time-frequency resolution, allowing for detailed signal decomposition, while the Sigmoid Kernel SVM provides robust classification capabilities. …”
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A Data-Driven-Based Grounding Fault Location Method for the Auxiliary Power Supply System in an Electric Locomotive by Xinyao Hou, Yang Meng, Qiang Ni

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Continuous wave mud pulse data transmission method based on continuous gradation frequency keying modulation and Convolution neural network demodulation by Yingzhong Zhu, Zhenhua Xia, Yue Yang, Cihao Zhu, Guoqing Cai, Shuang Hu, Junhao Wang

“…This method employs Continuous Gradation Frequency Keying (CGFK) modulation combined with Convolution Neural Network (CNN) demodulation for continuous mud pulse data transmission. …”
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Frequency hopping modulation recognition based on time-frequency energy spectrum texture feature by Hongguang LI, Ying GUO, Ping SUI, Zisen QI

“…For frequency hopping modulation identification,a novel method based on time-frequency energy spectrum texture feature was proposed.Firstly,the time-frequency diagram of the frequency hopping signal was obtained by smoothed pseudo Wigner-Ville distribution,and the background noise of the time-frequency diagram was removed by two-dimensional Wiener filtering to improve the resolution of the time-frequency diagram under low SNR conditions.Then,the connected-domain detection algorithm was used to extract the time-frequency energy spectrum of each hop signal and convert it into a time-frequency gray-scale image.The histogram statistical features and the gray-scale co-occurrence matrix feature were combined to form a 22-dimensional eigenvector.Finally,the feature set was trained,classified and identified by optimized support vector machine classifier.Simulation experiments show that the multi-dimensional feature vector extracted by the algorithm has strong representation ability and avoids the misjudgment caused by the similarity of single features.The average recognition accuracy of the six modulation methods of frequency hopping signals BPSK,QPSK,SDPSK,QASK,64QAM and GMSK is 91.4% under the condition of -4 dB SNR.…”
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A New Modelling and Feature Extraction Method Based on Complex Network and Its Application in Machine Fault Diagnosis by Qi Zhang, Tian Tian, Guangrui Wen, Zhifen Zhang

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Legal Judgment Prediction using Natural Language Processing and Machine Learning Methods: A Systematic Literature Review by Nasa Zata Dina, Sri Devi Ravana, Norisma Idris

“…There were 21 NLP methods applied, emphasizing the highest implementation of Term Frequency-Inverse Document Frequency (TF-IDF) while the most implemented ML method was Support Vector Machine (SVM). …”
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A Novel Method for Mechanical Fault Diagnosis Based on Variational Mode Decomposition and Multikernel Support Vector Machine by Zhongliang Lv, Baoping Tang, Yi Zhou, Chuande Zhou

“…A novel fault diagnosis method based on variational mode decomposition (VMD) and multikernel support vector machine (MKSVM) optimized by Immune Genetic Algorithm (IGA) is proposed to accurately and adaptively diagnose mechanical faults. …”
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Error Separation Method for Geometric Distribution Error Modeling of Precision Machining Surfaces Based on K-Space Spectrum by Zhichao Sheng, Jian Xiong, Zhijing Zhang, Taiyu Su, Min Zhang, Qimuge Saren, Xiao Chen

“…The effectiveness of the method was experimentally verified using two sets of machined surfaces. …”
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A Soft Start Method for Doubly Fed Induction Machines Based on Synchronization with the Power System at Standstill Conditions by José M. Guerrero, Kumar Mahtani, Itxaso Aranzabal, Julen Gómez-Cornejo, José A. Sánchez, Carlos A. Platero

“…In this paper, a soft start method for DFIM, inspired by the traditional synchronization method of synchronous machines, is proposed. …”
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CALCULATION OF TEMPERATURE REGIME AND DETERMINATION OF FREQUENCIES OF OWN VIBRATIONS OF THE SINGLE DIFFERENTIAL REZINOMETALLIC VIBROIZOLATOR by P. D. Balakin, L. V. Krasotina, A. V. Krivtsov

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Technical parameters analyses of different types of impact-vibration soil compacting machines by I. S. Tyuremnov

“…It is necessary to analyze the main technical characteristics of impactvibration machines of different types in order to assess the possibility of developing a mathematical model of soil compaction, combining several types of impact-vibration machines at once.Materials and methods. …”
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A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching by Chengyao Liu, Fei Dong, Kunpeng Ge, Yuanyuan Tian

“…To overcome these problems, by integrating the superiority of deep learning method and feature-based transfer learning method, this work proposes an innovative cross-domain fault diagnosis framework based on deep transfer convolutional neural network and supervised joint matching. …”
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Effect of Imputation Methods in the Classifier Performance by Pinar Cihan, Oya Kalıpsız, Erhan Gökçe

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Early Detection of ITSC Faults in PMSMs Using Transformer Model and Transient Time-Frequency Features by Ádám Zsuga, Adrienn Dineva

“…Existing diagnostic approaches, including motor current signature analysis (MCSA) and wavelet-based methods, are primarily designed for steady-state conditions and rely on manual feature selection, limiting their applicability in real-time embedded systems. …”
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Radiomics Feature Extraction from Ultrasound B-Mode Images and Radio-Frequency Signals of the Carotid Arterial Wall: A Feasibility Study by Maryam Jadoon, Federica Poli, Pierre Boutouyrie, Hakim Khettab, Elisabetta Bianchini, Francesco Faita, Xavier Jouven, Jean Philippe Empana, Rosa Maria Bruno

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AML-DECODER: Advanced Machine Learning for HD-sEMG Signal Classification—Decoding Lateral Epicondylitis in Forearm Muscles by Mehdi Shirzadi, Mónica Rojas Martínez, Joan Francesc Alonso, Leidy Yanet Serna, Joaquim Chaler, Miguel Angel Mañanas, Hamid Reza Marateb

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Method for EEG signal recognition based on multi-domain feature fusion and optimization of multi-kernel extreme learning machine by Shan Guan, Tingrui Dong, Long-kun Cong

“…Secondly, multivariate autoregressive (MVAR) model, wavelet packet decomposition, and Riemannian geometry methods are used to extract features from the time domain, frequency domain, and spatial domain, respectively, to construct a joint time-frequency-space feature vector. …”
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Improved railway track faults detection using Mel-frequency cepstral coefficient and constant-Q transform features by Rahman Shafique, Khadija Kanwal, Venkata Chunduri, Gyu Sang Choi, Imran Ashraf

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