An Improved Methodology to Locate Faults in Onshore Wind Farm Collector Systems by Moisés Davi, Alailton Júnior, Caio Grilo, Talita Cunha, Leonardo Lessa, Mário Oleskovicz, Denis Coury

“…The proposed methodology, which combines the various fault location methods tailored to specific fault types, results in a substantial improvement, achieving an average fault location error of 1.89%, reflecting a 92% reduction in error compared to conventional methods. …”
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Enabling efficient sizing of hybrid power plants: a surrogate-based approach to energy management system modeling by C. Assaad, J. P. M. Leon, J. Quick, T. Göçmen, S. Ghazouani, K. Das

“…This surrogate achieves a normalized root mean square error of 0.81 % in approximating yearly revenues. …”
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AI-Powered Forecasting of Environmental Impacts and Construction Costs to Enhance Project Management in Highway Projects by Joon-Soo Kim

“…The optimal ANN yielded average error rates of 29.8% for EL and 21.0% for CC at the design stage. …”
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Intra-Symbol Differential Amplitude Shift Keying-Aided Blind Detector for Ambient Backscatter Communication Systems by Shuaijun Ma, Peng Wei, Sa Xiao, Jianquan Wang, Wanbin Tang, Wei Xiang

“…For traditional detectors, the incorporation of pilot sequences results in a reduction in spectral efficiency. Furthermore, traditional energy-based detectors are inherently susceptible to a notable error floor issue, attributed to the co-channel direct link interference (DLI). …”
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An Investigation of Interpolation Techniques to Generate 2D Intensity Image From LIDAR Data by Imran Ashraf, Soojung Hur, Yongwan Park

“…Moreover, 3-D to 2-D data transformation also involves data reduction, which further deteriorates the quality of images. …”
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AE-BPNN: autoencoder and backpropagation neural network-based model for lithium-ion battery state of health estimation by Abdullah Ahmed Al-Dulaimi, Muhammet Tahir Guneser, Raghad Al-Shabandar, Yeonghyeon Gu, Muhammad Syafrudin, Norma Latif Fitriyani

“…The AE-BPNN model demonstrated significant advantages over Gaussian Process Regression (GPR) and Support Vector Regression (SVR), yielding lower Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), alongside higher R² scores. …”
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NSA-CHG: An Intelligent Prediction Framework for Real-Time TBM Parameter Optimization in Complex Geological Conditions by Youliang Chen, Wencan Guan, Rafig Azzam, Siyu Chen

“…Validation experiments employing field data from the Pujiang Town Plot 125-2 Tunnel Project demonstrated superior performance metrics, including 92.4% ± 1.8% warning accuracy for fractured zones, ≤28 ms optimization response time, and ≤4.7% relative error in energy dissipation analysis. Comparative analysis revealed a 32.7% reduction in root mean square error (<i>p</i> < 0.01) and 4.8-fold inference speed acceleration relative to conventional methods, establishing a novel data–physics fusion paradigm for TBM control with substantial implications for intelligent tunnelling in complex geological formations.…”
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Enhancing Time Series Product Demand Forecasting With Hybrid Attention-Based Deep Learning Models by Xuguang Zhang, Pan Li, Xu Han, Yongbin Yang, Yiwen Cui

“…Experimental results show that the HA-LSTM outperforms state-of-the-art baselines, including ARIMA, Prophet, and vanilla LSTM models, achieving a 15% improvement in Mean Absolute Percentage Error (MAPE) and a 12% reduction in Root Mean Square Error (RMSE). …”
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A study on the prediction of mountain slope displacement using a hybrid deep learning model by Yuyang Ma, Xiangxiang Hu, Yuhang Liu, Yaya Shi, Zhiyuan Yu, Xinmin Wang, Liangbai Hu, Shuailing Liu, Dongdong Pang

“…On the long-term prediction task at station JC03, it achieves a 37.9% reduction in Root Mean Square Error (RMSE), a 32.4% decrease in Mean Absolute Error (MAE), and a 4.6% increase in the coefficient of determination (R2) compared to the baseline LSTM. …”
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Optimizing HVAC energy efficiency in low-energy buildings: a comparative analysis of reinforcement learning control strategies under Tehran climate conditions by Mohammad Anvar Adibhesami, Amir Hassanzadeh

“…Model B (DRL) demonstrated a 50 percent improvement in prediction accuracy over Model A, with a mean absolute error of 0.579366 compared to 1.140008 and a root mean square error of 0.689770 versus 1.408069. …”
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Improved tooth flank region segmentation method of interference image by U-net neural network by YANG Pengcheng, ZHANG Jinjing, LI Xiaocheng, MENG Jie, KANG Leqian

“…Laser interferometry is an effective method to measure the tooth flank topography errors, and the information of tooth flank topography can be obtained accurately by using interferometric image processing technology. …”
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Real-Time Operational Trial of Atmosphere–Ocean–Wave Coupled Model for Selected Tropical Cyclones in 2024 by Sin Ki Lai, Pak Wai Chan, Yuheng He, Shuyi S. Chen, Brandon W. Kerns, Hui Su, Huisi Mo

“…It is shown that the track error of the UWIN-CM was lower than other regional numerical weather prediction (NWP) models in operation at the HKO, with a reduction in mean direct positional error of up to 50% for the first 48 forecast hours. …”
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A Multichannel Approach and Testbed for Centimeter-Level WiFi Ranging by Jayson P. Van Marter, Matan Ben-Shachar, Yaron Alpert, Anand G. Dabak, Naofal Al-Dhahir, Murat Torlak

“…Utilizing our proposed PCMC technique with 16 channels, we demonstrate a median error of 2.7 cm and a 90th percentile error of 9.5 cm in indoor line-of-sight conditions.…”
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Analysis of Unmanned Surface Vehicles Heading KF-Based PI-(1+PI) Controller Using Improved Spider Wasp Optimizer by Xiaoyu Li, Xiangye Zeng, Jingyi Wang, Qi Li, Baoshuo Fan, Qi Zeng

“…A transfer function model of the USV heading system is established using voyage data, with ISWO optimizing its parameters, achieving a 5.67% reduction in mean squared error (MSE) compared to the original Spider Wasp Optimizer and outperforming classical algorithms like Arithmetic Optimization Algorithm (AOA), Crayfish Optimization Algorithm (COA), and Marine Predators Algorithm (MPA). …”
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Robust Driving Control Design for Precise Positional Motions of Permanent Magnet Synchronous Motor Driven Rotary Machines with Position-Dependent Periodic Disturbances by Syh-Shiuh Yeh, Zhi-Hong Liu

“…RDC not only suppresses position-dependent periodic disturbances, but also significantly reduces position errors with a reduction rate of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>90</mn><mo>%</mo></mrow></semantics></math></inline-formula>. …”
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Configurational Comparison of a Binary Logic Transmission Unit Applicable to Agricultural Tractor Hydro-Mechanical Continuously Variable Transmissions and Its Wet Clutch Optimizati... by Wenjie Li, Zhun Cheng, Mengchen Yang

“…Under light-load conditions, the optimized GRNN reduced total relative error by 39.6%, while under heavy-load conditions, it achieved a 61% reduction. …”
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Research on a Surface Roughness Measurement Under ResNet-Based Roughness Classification and Light-Section With Seam-Driven Image Stitching (RCLS) by Huashen Guan, Qiushen Cai, Xiaobin Li, Guofu Sun

“…The light section method had a reduction of 2.75% on the mean relative error compared to stylus and RCLS could further reduce the mean relative error by 1.42%, especially in planned sample. …”
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Autoencoder-Driven Fiducial Landmark Identification in 3D Brain MRI for Neuroimaging Alignment by G. Deepali, H. Anitha, B. P. Swathi, M. V. Suhas

“…However, manual annotation of these landmarks is time-consuming, prone to human error, and further complicated by variations in MRI acquisition protocols and incomplete head coverage. …”
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Innovative approaches to beam forming antenna array systems with adaptive Partial Update NLMS algorithms by Zahraa A. Shubber, Thamer M. Jamel, Ali.K. Nahar

“…Using a Uniform Linear Array (ULA) Antennas in a simulation environment, we find that the, in terms of Mean Square Error (MSE), convergence rate, and steady-state error, it is evident that all PU NLMS algorithms (with the exception of Periodic-NLMS) had performed close, and approximately equivalent performance to the full band NLMS algorithms. …”
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NSMO-Based Adaptive Finite-Time Command-Filtered Backstepping Speed Controller for New Energy Hybrid Ship PMSM Propulsion System by Dan Zhang, Suijun Xiao, Hongfen Bai, Diju Gao, Baonan Wang

“…The results of this study demonstrate a significant reduction in speed-tracking overshoot to zero, a substantial decrease in integral squared error by 90.15%, and a notable improvement in response time by 18.6%.…”
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