Enhancing DC Motor Speed Control Performance Using Heuristic Optimization and Comparative Analysis of Control Methods by Ali Öztürk, Nasser Bandar Nasser Al Saremi, Beytullah Bozali

“…Direct Current (DC) motors are an important component that converts electrical energy into mechanical energy, used in a wide range of applications from industrial applications to home appliances. …”
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Chaos Time Series Prediction Based on Membrane Optimization Algorithms by Meng Li, Liangzhong Yi, Zheng Pei, Zhisheng Gao, Hong Peng

“…The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE), root mean square error (RMSE), and mean absolute percentage error (MAPE).…”
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Analysis of Universal Decoding Techniques for 6G Ultra-Reliable and Low-Latency Communication Scenario by Abhilasha Gautam, Prabhat Thakur, Ghanshyam Singh

“…Universal decoders work on a wide range of error-correcting codes, making them scalable for different communication protocols. …”
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An Expansion Formula with Higher-Order Derivatives for Fractional Operators of Variable Order by Ricardo Almeida, Delfim F. M. Torres

“…An estimation for the error is given. The efficiency of the approximation method is illustrated with examples. …”
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Streaming quantum state purification by Andrew M. Childs, Honghao Fu, Debbie Leung, Zhi Li, Maris Ozols, Vedang Vyas

“…We derive an efficient purification procedure based on the swap test for qudits of any dimension, starting with any initial error parameter. Treating the initial error parameter and the dimension as constants, we show that our procedure has sample complexity asymptotically optimal in the final error parameter. …”
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On a Mixed Poisson Liu Regression Estimator for Overdispersed and Multicollinear Count Data by Ramajeyam Tharshan, Pushpakanthie Wijekoon

“…To estimate the Liu parameter, some exiting methods are used, and the superiority conditions of the new estimator over the MLE and PMQL ridge regression estimator are obtained based on the mean square error (MSE) criterion. A Monte Carlo simulation study and applications are used to assess the performance of the new estimator in the scalar mean square error (SMSE) sense. …”
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Bridge facility health detection method based on DATA random subspace by Xiaoyan Shen

“…In the case of bridge deck cracks, the positioning error is 1.11 m and the damage degree error is 4.83 %. …”
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Machine learning-based stem taper model: a case study with Brutian pine by Fadime Sağlam

“…The results show that the XGBoost model outperforms all other approaches, demonstrating superior predictive accuracy with minimal error, as indicated by lower root mean square error (RMSE), mean absolute error (MAE), and bias values. …”
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A Gbps Building-to-Building VLC Link Using Standard CMOS Avalanche Photodiodes by Bassem Fahs, Matthew Romanowicz, Mona M. Hella

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New Forms of the Open Newton-Cotes-Type Inequalities for a Family of the Quantum Differentiable Convex Functions by Jarunee Soontharanon, Muhammad Ali, Shahram Rezapour, Muhammad Toseef, Thanin Sitthiwirattham

“…This work distinguishes itself from existing studies by employing quantum operators, leading to sharper and more precise error estimates. These results extend the applicability of Newton-Cotes methods to quantum calculus, offering a novel contribution to the numerical analysis of convex functions. …”
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Wheel flat detection using long short-term memory and transformer models with a 1:10 scale railway test rig by Yong Cui, Euiyoul Kim, Shizhe Yan, Qing Yu

“…The findings indicate that the Transformer model significantly outperforms the Long Short-Term Memory model, especially when feature-level sensor fusion is employed, achieving an average error as low as 0.0069 mm with percentage of error at 5.30%, minimizing the maximum error to 0.0985 mm. …”
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Closed Loop Direct Adaptive Inverse Control for Linear Plants by Muhammad Amir Shafiq, Muhammad Shafiq, Nisar Ahmed

“…CDAIC is applicable to stable or stabilized, minimum or nonminimum phase linear plants. …”
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State of Charge Estimation in Li-Ion Batteries Using a Parallel LSTM-Based Approach: The Impact of Modeling Based on Operating States by Osman Ozer, Hayri Arabaci

“…In Li-ion battery applications, effective energy management relies heavily on accurate knowledge of the state of charge (SOC). …”
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Optimization of process parameters in 3D-nanomaterials printing for enhanced uniformity, quality, and dimensional precision using physics-guided artificial neural network by Anita Ghandehari, Jorge A. Tavares-Negrete, Jerome Rajendran, Qian Yi, Rahim Esfandyarpour

“…Traditional trial-and-error methods often result in significant material waste and time consumption. …”
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Location estimation of UWB-based wireless capsule endoscopy using TDoA in various gastrointestinal simulation models. by Sruthi Krishnan, Mohammed Abdel-Hafez, Matti Hämäläinen

“…Positioning receivers in three rows reduces the estimation error by 44%, and positioning the reference receiver in the middle row reduces it by 33%. …”
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Pressure-Related Discrepancies in Landsat 8 Level 2 Collection 2 Surface Reflectance Products and Their Correction by Santosh Adhikari, Larry Leigh, Dinithi Siriwardana Pathiranage

“…Landsat 8 Level 2 Collection 2 (L2C2) surface reflectance (SR) products are widely used in various scientific applications by the remote sensing community, where their accuracy is vital for reliable analysis. …”
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A Method for Localization of Cellular Users From Call Detail Records by Steven W. Ellingson

“…Of the 16 sectors evaluated, the aggregate error rate (i.e., fraction of users located outside the RPL estimated for the associated sector) is found to be 1.3%, with worst per-sector error rate of about 13.3% and error rates below 1.8% for 13 of the 16 sectors. …”
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Optimal I-V Curve Scan Time of Solar Cells and Modules in Light of Irradiance Level by Matic Herman, Marko Jankovec, Marko Topič

“…A too fast I-V curve measurement can thus introduce a significant error due to its internal capacitances. This paper analyses the I-V curve error of a measured solar cell or module in light of scan time and irradiance level. …”
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Improving Indoor WiFi Localization by Using Machine Learning Techniques by Hanieh Esmaeili Gorjan, Víctor P. Gil Jiménez

“…This paper presents a novel architecture for indoor positioning, leveraging machine learning techniques and a divide-and-conquer strategy to achieve low error estimates. The proposed method achieves an MAE (mean absolute error) of approximately 1 m for latitude and longitude. …”
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Leveraging Large Language Models for Discrepancy Value Prediction in Custody Transfer Systems: A Comparative Analysis of Probabilistic and Point Forecasting Approaches by Fiki Hidayat, Arbi Haza Nasution, Fajril Ambia, Dike Fitriansyah Putra, Mulyandri

“…The evaluation covers metrics such as Weighted Quantile Loss (WQL), Scaled Quantile Loss (SQL), Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (SMAPE), and Root Mean Square Error (RMSE). …”
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