Parallel Farby–Perot Interferometers in an Etched Multicore Fiber for Vector Bending Measurements by Kang Wang, Wei Ji, Cong Xiong, Caoyuan Wang, Yu Qin, Yichun Shen, Limin Xiao

“…The reconstruction results of nine randomly selected pairs of bending magnitudes and directions show that the average relative error of magnitude is ~4.5%, and the average absolute error of orientation angle is less than 2.0°. …”
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Evaluating the Thermal Shock Resistance of SiC-C/CA Composites Through the Cohesive Finite Element Method and Machine Learning by Qiping Deng, Yu Xiong, Zirui Du, Jinping Cui, Cheng Peng, Zhiyong Luo, Jinli Xie, Hailong Qin, Zhimin Sun, Qingfeng Zeng, Kang Guan

“…Silicon carbide-coated carbon fiber-reinforced carbon aerogel (SiC-C/CA) composites are ideal for high-temperature applications due to their ability to endure rapid temperature changes without losing structural integrity. …”
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Mechanical behavior of material removal under various rake angle diamond tool ultra-precision cutting of titanium alloy by Lingyi Sun, Xin Cui, Chunjin Wang, Yanbin Zhang, Changhe Li

“…The results show that the model achieves an average prediction error of 8.23 %, with a minimum error of 3.54 %. …”
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Design of a Robust Unknown Input Observer for the State of Charge Estimation for Lithium-Ion Batteries by Omid Rezaei, Mohammadali Faghih

“…Also, for large-scale applications such as electric vehicles, disturbances in measurement may increase the SoC estimation error. …”
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Machine learning models for accurately predicting properties of CsPbCl3 Perovskite quantum dots by Mehmet Sıddık Çadırcı, Musa Çadırcı

“…Although all models performed highly accurate results, SVR and NND demonstrated the best accurate property prediction by achieving excellent performance on the test and training datasets, with high R2, low Root Mean Squared Error (RMSE) and low Mean Absolute Error (MAE) metric values. …”
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Quality of service optimization algorithm based on deep reinforcement learning in software defined network by Cenhuishan LIAO, Junyan CHEN, Guanping LIANG, Xiaolan XIE, Xiaoye LU

“…Deep reinforcement learning has strong abilities of decision-making and generalization and often applies to the quality of service (QoS) optimization in software defined network (SDN).However, traditional deep reinforcement learning algorithms have problems such as slow convergence and instability.An algorithm of quality of service optimization algorithm of based on deep reinforcement learning (AQSDRL) was proposed to solve the QoS problem of SDN in the data center network (DCN) applications.AQSDRL introduces the softmax deep double deterministic policy gradient (SD3) algorithm for model training, and a SumTree-based prioritized empirical replay mechanism was used to optimize the SD3 algorithm.The samples with more significant temporal-difference error (TD-error) were extracted with higher probability to train the neural network, effectively improving the convergence speed and stability of the algorithm.The experimental results show that the proposed AQSDRL effectively reduces the network transmission delay and improves the load balancing performance of the network than the existing deep reinforcement learning algorithms.…”
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Dual-Vector-Based Model Predictive Current Control with Online Parameter Identification for Permanent-Magnet Synchronous Motor Drives in Marine Electric Power Propulsion System by Shengqi Huang, Yuanwei Zhang, Lei Shi, Yuqing Huang, Bin Chang

“…To reduce torque ripple, the DV combination was generated based on the error current vector, and the action time was allocated in accordance with the minimum root mean square error of the current. …”
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SOH and RUL Estimation for Lithium-Ion Batteries Based on Partial Charging Curve Features by Kejun Qian, Yafei Li, Qiheng Zou, Kecai Cao, Zhongpeng Li

“…Experimental validation on public datasets demonstrates superior performance of the methodology described above, with an SOH estimation root mean square error (RMSE) and mean absolute error (MAE) below 1.42% and 0.52% and RUL estimation relative error (RE) under 1.87%. …”
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Multi-physical modeling and analysis on UAV launching system by Chin-Fa Lee, Jhy-Cherng Tsai, Cheng-Hsiung Kuo

“…Using MATLAB as the platform, this multi-physical model accurately predicts the velocity of the launching system (or dummy), with a maximum error of 9.0% and a minimum error of 4.3% compared to the experimental results. …”
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An Effective Solution of the Cube-Root Truly Nonlinear Oscillator: Extended Iteration Procedure by B. M. Ikramul Haque, M. M. Ayub Hossain

“…Also, the comparison of the obtained analytical solutions with the numerical results represented an extraordinary accuracy. The percentage error for the fourth approximate frequency of cube-root truly nonlinear oscillator is 0.006 and the percentage error for the fourth approximate frequency of inverse cube-root truly nonlinear oscillator is 0.12.…”
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A 4×256 Gbps silicon transmitter with on-chip adaptive dispersion compensation by Shihuan Ran, Yu Guo, Yuanbin Liu, Ting Miao, Yangbo Wu, Yang Qin, Yuyao Guo, Liangjun Lu, Yixiao Zhu, Yu Li, Qunbi Zhuge, Jianping Chen, Linjie Zhou

“…At 1271 nm (−3.99 ps/nm/km), the proposed transmitter enabled 4 × 256 Gbps transmission over 5 km fiber, achieving bit error ratio below both the soft-decision forward-error correction threshold with feed-forward equalization (FFE) alone and the hard-decision forward-error correction threshold when combining FFE with maximum-likelihood sequence detection. …”
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Regression analysis and artificial neural networks for predicting pine species volume in community forests by Wenceslao Santiago-García

“…The relative rank method was used to identify the most effective models based on goodness-of-fit statistics, including the coefficient of determination (R2), average absolute error (AAE), total relative error (TRE), average systematic error (ASE), and mean percent standard error (MPSE). …”
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A High-Accuracy MOC/FD Method for Solving Fractional Advection-Diffusion Equations by Lijuan Su, Pei Cheng

“…The stability, consistency, convergence, and error estimate of the method are obtained. An example is also given to illustrate the applicability of theoretical results.…”
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High‐Throughput Nanorheology of Living Cells Powered by Supervised Machine Learning by Jaime R. Tejedor, Ricardo Garcia

“…Despite its popularity, some applications on mechanobiology are limited by the low throughput of the technique. …”
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Artificial neural networks employment in the prediction of evapotranspiration of greenhouse-grown sweet pepper by Héliton Pandorfi, Alan C. Bezerra, Roberto T. Atarassi, Frederico M. C. Vieira, José A. D. Barbosa Filho, Cristiane Guiselini

“…ABSTRACT This study aimed to investigate the applicability of artificial neural networks (ANNs) in the prediction of evapotranspiration of sweet pepper cultivated in a greenhouse. …”
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Steganography Scheme Based on Reed-Muller Code with Improving Payload and Ability to Retrieval of Destroyed Data for Digital Images by A. M. Molaei, M. H. Sedaaghi, H. Ebrahimnezhad

“…Therefore, the proposed algorithm has a blind detection process which is more suitable for practical and online applications. The simulation results show that the proposed algorithm is also able to retrieve destroyed data by intentional or unintentional attacks such as addition of noise and filtering due to use of the error correction code. …”
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Quality Evaluation and Predictive Analysis of Drilled Holes in Jute/Palm/Polyester Hybrid Bio-Composites Using CMM and ANN Techniques by Salah Amroune, Abdelmalek Elhadi, Mohamed Slamani, Mustapha Arslane, Ahmed Belaadi, Mahmood M. S. Abdullah, Hamad A. Al-Lohedan, Tarek Bidi, Herbert Mukalazi, Amar Al-Khawlani

“…The results show the influence of feed rate on the delamination factor (Fd) (R2 = 0.98), circularity error (R2 = 0.99), and cylindricity error (R2 = 0.98). …”
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Analysis of meteorological effects of cosmic ray neutron component based on data from mid-latitude stations by Kobelev P. G., Hamraev Y. B., Yanke V. G.

“…Precision neutron monitors providing continuous monitoring with a statistical accuracy of ~0.15 %/hr are effective for studying cosmic ray variations; therefore, contributions from other error sources should not exceed the contribution of this statistical error. …”
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Snake-inspired mobile robot positioning with hybrid learning by Aviad Etzion, Nadav Cohen, Orzion Levi, Zeev Yampolsky, Itzik Klein

“…Therefore, due to noises and other error terms associated with the inertial readings, the navigation solution drifts in time. …”
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Failure analysis of hybrid fiber reinforced polymer composite tubes subjected to quasi-static compressive load: an experimental study by S. Sivalingam, T. P. Sathishkumar, L. Rajeshkumar, M. Sathishkumar

“…Such short composite columns find their applications in roll-over protection in automobiles, as retrofits in construction applications and developing modular structural designs.…”
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