基于多重分形谱的转子系统故障诊断与参数优选 by 刘岩, 王金东, 郭建华, 胡清明

“…Because of the robustness of one dimensional time series reconstruction G-P algorithm to extract the fault omen is poor,especially influenced by a sensitive noise in the measured signal.A noise reduction method is proposed based on detrended fluctuat ion analysis（FDA） and kernel principal component analysis（KPCA）,the eigenvalue extraction algorithm based on Mult ifractal spectrum is presented.Through pseudo-phase portrait to determine the weighting factor threshold,optimize and choose parameter and compare with the defects of single G_P algorithm,and combine with the 3 kinds of rotor system common faults,the stability and accuracy of eigenvalue extraction of this method is analyzed,the results prove that,the diagnosis result is good.…”
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基于正态反高斯模型的自适应小波消噪方法 by 吴国洋

“…A locally adaptive wavelet de-noising method based on normal inverse Gaussian modal is proposed.Firstly,the db5 wavelet is used to decompose the signal.For those wavelet coefficients which contain a lot of noise,the normal inverse Gaussian modal with good approximation property is constructed as the prior distribution model of those coefficients,on the basis of the model,Bayesian maximum a posteriori estimator is used to estimate the noisy wavelet coefficients and got the realistic wavelet coefficients.Then in the process of posteriori estimation,in order to get the best posteriori approximation model,the particle swarm optimization algorithm is used to select the key coefficient of the model.Finally,new wavelet coefficients are used for the reconstruction of the de-noised signal,and the de-noised signal is gotten.The algorithm is analyzed by simulation and bearing fault signal respectively.Analysis results show that this algorithm has good noise reduction effect,and can efficiently reduce the noise.…”
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Deep-Learning-Based Approach in Imaging Radiometry by Aperture Synthesis: Application to Real SMOS Data by Ali Khazaal, Richard Faucheron, Nemesio J. Rodriguez-Fernandez, Eric Anterrieu

“…A novel image reconstruction algorithm for aperture synthesis measurements using deep learning techniques was introduced recently. …”
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Analyzing Digital Political Campaigning Through Machine Learning: An Exploratory Study for the Italian Campaign for European Union Parliament Election in 2024 by Paolo Sernani, Angela Cossiri, Giovanni Di Cosimo, Emanuele Frontoni

“…Our findings highlight the significance of micro-targeting practices, the role of algorithmic biases, and the risks associated with disinformation in shaping public opinion. …”
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Outdoor Clothing Design for Traffic Safety Based on Big Data and Artificial Intelligence by Ya Zhou, Yun Shi

“…The first experiment is to test the accuracy of various machine learning algorithms for clothing size measurement. The results obtained are as follows: the predicted value of the DBN neural network is the closest to the actual value, the average prediction accuracy of DBN for the cuff size is 90%, and the average prediction accuracy for the neck circumference is 91.5%. …”
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Ensemble machine learning model for forecasting wind farm generation by A. G. Rusina, Osgonbaatar Tuvshin, P. V. Matrenin, N. N. Sergeev

“…This study is carried out by ensemble algorithms, such as Random Forest, AdaBoost and XGBoost, which are one of the machine learning approaches. …”
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Design of the Urban Lighting Control System Based on Optical Multisensor Technology and the GM Model by Weili Wu, Xiang Tang

“…The results of the evaluations show that the fresh method is successful in dimming, prediction, and control. This conclusively demonstrates the efficacy of the paper’s proposed design solution, which integrates optical multisensor technology with sophisticated control algorithms and data analysis to improve the quality of life in urban areas by boosting the efficiency and sustainability of the urban lighting system.…”
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Application of Particle Filter to On-Board Life Estimation of LED Lights by Fazludeen Ruknudeen, Sundararajan Asokan

“…Life prediction methods based on reliability analysis and filtering algorithms proposed in the past were predominantly based on accelerated life tests and cannot be readily implemented for on-board diagnosis. …”
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Modeling Techniques and Boundary Conditions in Abdominal Aortic Aneurysm Analysis: Latest Developments in Simulation and Integration of Machine Learning and Data-Driven Approaches by Burcu Ramazanli, Oyku Yagmur, Efe Cesur Sarioglu, Huseyin Enes Salman

“…Accurate BCs can enhance ML predictions, increasing the clinical applicability. …”
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Advanced Supply Chain Management Using Adaptive Serial Cascaded Autoencoder with LSTM and Multi-Layered Perceptron Framework by Aniruddha Deka, Parag Jyoti Das, Manob Jyoti Saikia

“…The SGSO-ASCALSMLP model is highly efficient in predicting financial risks, outperforming conventional prediction techniques and heuristic algorithms, making it a promising approach for enhancing financial risk management in supply chain networks.…”
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Rediscovering histology – the application of artificial intelligence in inflammatory bowel disease histologic assessment by Giovanni Santacroce, Irene Zammarchi, Olga Maria Nardone, Ivan Capobianco, Miguel Puga-Tejada, Snehali Majumder, Subrata Ghosh, Marietta Iacucci

“…Additionally, AI shows promise in predicting adverse outcomes and therapeutic responses, making it a promising tool for clinical practice and clinical trials. …”
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Multi-omics analysis untangles the crosstalk between intratumor microbiome, lactic acid metabolism and immune status in lung squamous cell carcinoma by Xun Qiu, Dan Li

“…Multiple machine learning algorithms were used to generate the LUSC classification. …”
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Harnessing Digital Twin Technology for Detailed Simulation and Analysis of Climate Impact on Agricultural Systems by Hassan Muntather Muhsin, Ramadan Ghazi Mohamad, Karthikeyan R.

“…The model incorporates real time data collection and well developed, yet still new, algorithms like random forest to enable real time analysis, as well as realistic prediction using system simulation models to yield highly useful and actionable insights. …”
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Evaluating the strength properties of high-performance concrete in the form of ensemble and hybrid models using deep learning techniques by Zhe Wang, Tao Sun, Yan Sun, Na Liu

“…Deep learning techniques, including hybrid and ensemble methods, were developed to predict these properties with high accuracy. This paper focuses on forecasting models using T-SFIS, GBMBoost, and Decision Tree, combined with metaheuristic algorithms (GWO, QPSO) in hybrid and ensemble frameworks. …”
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Data-Driven Diagnostics for Pediatric Appendicitis: Machine Learning to Minimize Misdiagnoses and Unnecessary Surgeries by Deborah Maffezzoni, Enrico Barbierato, Alice Gatti

“…In this study, we explore the potential of machine learning (ML) algorithms to improve the diagnosis, management, and prediction of appendicitis severity in pediatric patients. …”
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Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRI by Dayeong An, El-Sayed Ibrahim

“…Despite normal global left ventricular ejection fraction in both groups, strain analysis showed significant reductions in the anteroseptal and anterolateral segments of irradiated rats. …”
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The Method of Elimination of Vibrations and Forces Excited by Unbalanced Rotary Machines with Inaccesible Rotor by J. Michalczyk, G. Cieplok

“… Possibilities of application of the method of synchronous elimination to the reduction of vibrations and forces in unbalanced rotary machines with inaccessible axes were considered in this paper. …”
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A spatio-temporal infection epidemic model with fractional order, general incidence, and vaccination analysis by Sara Soulaimani, Abdelilah Kaddar, Fathalla A. Rihan

“…Through simulations with a predictor–corrector algorithm, key insights into epidemiological dynamics are provided, elucidating the impact of vaccination on reducing disease transmission and altering R0. …”
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A Hybrid Deep Learning and Improved SVM Framework for Real-Time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization by Jianqiu Chen, Huan Xiong, Shixuan Zhou, Xiang Wang, Benxiao Lou, Longtang Ning, Qingwei Hu, Yang Tang, Guobin Gu

“…Multiscale features are then extracted with Inception v3 and combined with principal component analysis (PCA) for dimensionality reduction. Finally, an SVM classification algorithm is employed for personnel detection. …”
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Transitional Grid Maps: Joint Modeling of Static and Dynamic Occupancy by Jose Manuel Gaspar Sanchez, Leonard Bruns, Jana Tumova, Patric Jensfelt, Martin Torngren

“…Autonomous agents rely on sensor data to construct representations of their environments, essential for predicting future events and planning their actions. …”
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