Improving Trajectory Tracking of Differential Wheeled Mobile Robots With Enhanced GWO-Optimized Back-Stepping and FOPID Controllers by Li Qiang, Hooi Hung Tang, Nur Syazreen Ahmad

“…This hybrid approach optimizes controller parameters using a multi-metric cost function that incorporates Integral Absolute Error (IAE) and Integral Squared Error (ISE) to minimize steady-state error and enhance responsiveness to larger deviations. …”
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Dynamic mode decomposition based fault diagnosis in three-phase electrical machines by Saravanakumar Rajendran, Rhethika Sreejesh, V.S. Kirthika Devi, Debashisha Jena, David Banjerdpongchai

“…Three-phase electrical machines are widely used in various industrial applications, and the mechanical fault in those machines leads to an oscillation in the load torque, which introduces an amplitude and/or phase modulation in the stator current. …”
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Prescribed-Time-Based Anti-Disturbance Tracking Control of Manipulators Under Multiple Constraints by Zirui Wang, Haoran Zheng, Guangming Zhang

“…The PPC meets the rigorous requirements of error convergence during trajectory tracking by regulating the error dynamics, while the ESO is employed to estimate unknown disturbances and enhance the system’s resilience to interference. …”
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Self Attention-Driven ECG Denoising: A Transformer-Based Approach for Robust Cardiac Signal Enhancement by Aymane Edder, Fatima-Ezzahraa Ben-Bouazza, Idriss Tafala, Oumaima Manchadi, Bassma Jioudi

“…Experimental validation on real-world datasets demonstrates ECGDnet’s remarkable efficacy in noise suppression, achieving a Signal-to-Noise Ratio (SNR) of 19.83, a Normalized Mean Squared Error (NMSE) of 0.9842, a Reconstruction Error (RE) of 0.0158, and a Pearson Correlation Coefficient (PCC) of 0.9924. …”
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Adaptive Parameter Identification Based Tracking Control of Servo Systems with Unknown Actuator Backlash Compensation by Hailan Du, Liang Tao, Xiongfeng Deng, Binzi Xu

“…In high-precision positioning and rapid-response applications, backlash significantly compromises system performance. …”
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Parameter Optimization Method for Predictor–Corrector Guidance With Impact Angle Constraint by Xinwan Kong, Cheng Zhang

“…With the applications of predictor–corrector guidance technology on hypersonic vehicles, the stability and robustness of the predictor–corrector algorithm have become issues of concern. …”
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Estimation of parameters for 3D geomechanical modeling from triaxial test results by Artem Kukhtinskii

“…These innovations offer faster, more reliable results, reducing error and enhancing the comparability of analyses in geomechanics, with potential applications across various geological settings.…”
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Noisy Atomic Magnetometry with Kalman Filtering and Measurement-Based Feedback by Júlia Amorós-Binefa, Jan Kołodyński

“…To prove that our approach constitutes the optimal strategy in realistic scenarios, we derive ultimate bounds on the estimation error applicable in the presence of both local and collective decoherence, and show that these are indeed attained. …”
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On the approximation of sum of lognormal for correlated variates and implementation. by Asyraf Nadia Mohd Yunus, Nora Muda, Abdul Rahman Othman, Sonia Aïssa

“…This research provides practical guidance for selecting appropriate approximation methods when modeling correlated lognormal sums in diverse applications.…”
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Improving the criteria of electricity consumptionforecasting in petrochemical industrial units based ondeep learning by Ehsan Tavakoli Garmaserh, Mehran Emadi

“…Experimental evaluations using benchmark datasets demonstrate significant improvements, achieving a Root Mean Square Error (RMSE) of 0.0693 and a Mean Absolute Percentage Error (MAPE) reduction of over 15% compared to state-of-the-art methods. …”
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An interleaved physics-based deep-learning framework as a new cycle jumping approach for microstructurally small fatigue crack growth simulations by Vignesh Babu Rao, Ashley D. Spear

“…We show that this framework, representing a novel cycle-jumping approach, effectively limits error accumulation in history-dependent fatigue crack evolution and forms a template for other time-series applications in materials.…”
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Advanced removal of butylparaben from aqueous solutions using magnetic molybdenum disulfide nanocomposite modified with chitosan/beta-cyclodextrin and parametric evaluation through... by Saeed Hosseinpour, Alieh Rezagholizade-shirvan, Mohammad Golaki, Amir Mohammadi, Amir Sheikhmohammadi, Zahra Atafar

“…The model performance was assessed through Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and the statistical metrics R², adjusted R², Explained Variance Score (EVS). …”
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Analytical expressions for electrodynamic parameters of the shielded microstrip line by A. N. Kovalenko, A. D. Yarlykov

“…These results make it possible to establish the limits of applicability of the quasi-static approximation and to determine the error in calculating the deceleration coefficient and wave resistance using the obtained analytical expressions. …”
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Machine and deep learning models for predicting high pressure density of heterocyclic thiophenic compounds based on critical properties by Amir Hossein Sheikhshoaei, Ali Khoshsima

“…Models employed include Decision Tree (DT), Adaptive Boosting Decision Tree (AdaBoost-DT), Light Gradient Boosting Machine (LightGBM), Gradient Boosting (GBoost), TabNet, and Deep Neural Network (DNN). The statistical error evaluation showed that the LightGBM model showed superior performance with an average absolute percent relative error (AAPRE) of 0.0231, a root mean square error of 0.3499, and coefficient of determination (R2) of 0.9999. …”
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Performance of a Predictive Model for Long-Term Hemoglobin Response to Darbepoetin and Iron Administration in a Large Cohort of Hemodialysis Patients. by Carlo Barbieri, Elena Bolzoni, Flavio Mari, Isabella Cattinelli, Francesco Bellocchio, José D Martin, Claudia Amato, Andrea Stopper, Emanuele Gatti, Iain C Macdougall, Stefano Stuard, Bernard Canaud

“…Our model was able to predict individual variation of hemoglobin concentration 3 months in the future with a Mean Absolute Error (MAE) of 0.75 g/dL. Error analysis showed a narrow Gaussian distribution centered in 0 g/dL; a root cause analysis identified intercurrent and/or unpredictable events associated with hospitalization, blood transfusion, and laboratory error or misreported hemoglobin values as the main reasons for large discrepancy between predicted versus observed hemoglobin values. …”
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Machine Learning-Based Lithium Battery State of Health Prediction Research by Kun Li, Xinling Chen

“…The models were validated using the NASA PCoE battery aging datasets B0005, B0006, and B0007, with prediction accuracy evaluated based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²). …”
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Multi-fidelity graph neural networks for predicting toluene/water partition coefficients by Thomas Nevolianis, Jan G. Rittig, Alexander Mitsos, Kai Leonhard

“…Our results show that multi-target learning significantly improves predictive accuracy, achieving a root-mean-square error of 0.44 $$\log {P}$$ log P units for the EXT-Zamora, compared to a root-mean-square error of 0.63 $$\log {P}$$ log P units for single-task models. …”
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基于CMM的弧面分度凸轮精密测量软件的开发 by 唐琳, 王晓飞, 林家春

“…In order to guarantee the quality of roller gear cam,a measure software which can measure the geometry form of all kinds of roller gear cams is developed based on CMM.The software is introduced from 5 aspects,such as measurement principle,measurement definitions,software process,software functions and its applications.The software has the function of parameter input,automatic measure,error evaluate,print,data saving and so on.The software is applied in batch measuring of roller gear cam.…”
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A Sharp Simpson’s Second Type Inequality via Riemann–Liouville Fractional Integrals by Mohsen Rostamian Delavar

“…To demonstrate the applicability of the main result, three examples are given. …”
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Determination of the Parameters of Langmuir-Type Isotherms by the Frontal Analysis Method. Some Identification Aspects by Waldemar Nowicki, Grażyna Nowicka

“…The applicability of the classical frontal analysis method for the estimation of the parameters of various Langmuirian-type adsorption isotherms was examined. …”
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