Carbon emission prediction method of steel plants based on long short-term memory network by Fengyun LI, Zehui DOU, Peng LI, Wei GUO

“…As the second largest carbon emitter in China, iron and steel enterprises have great potential for carbon emission reduction.In order to facilitate the supervision and control of carbon emissions by relevant departments, carbon emission prediction research is carried out.Taking a steelmaking plant as the research object, firstly, the carbon dioxide emissions in the steelmaking process were analyzed, and 10 energy substances that caused carbon emissions were determined.The basic energy data of the steelmaking plant from 2001 to 2023 were collected, and the carbon emissions were calculated from the basic energy data according to the carbon emission accounting method.Secondly, based on the long short-term memory network to predict the carbon emissions in the next 7 years, the training error and test error were close to 0.01, and the actual error was 1 323 307.46 tons of carbon dioxide.Then, the Mann-Kendall trend test was used to evaluate the overall carbon emission trend of the steelmaking plant.Finally, some reasonable suggestions were put forward for steelmaking plants in order to actively respond to the goal of low-carbon environmental protection.…”
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Non-Contact Oxygen Saturation Estimation Using Deep Learning Ensemble Models and Bayesian Optimization by Andrés Escobedo-Gordillo, Jorge Brieva, Ernesto Moya-Albor

“…Thus, by leveraging Bayesian optimization for hyperparameter tuning and integrating a Bagging Ensemble, we achieved a significant reduction in the training error (bias), achieving a better generalization over the test set, and reducing the variance in comparison with the baseline model for SpO₂ estimation.…”
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Variable convergence train noise active control algorithm based on feedforward reference by LI Tao, HE Yuyao, WANG Ning, LI Zhuang, SHEN Zhaoyuan, XIAO Gang

“…Then it was applied to the low-frequency noise reduction of trains. Simulation results show that the algorithm can improve the balance between convergence speed, tracking speed and steady-state error. …”
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Advancing sustainable renewable energy: XGBoost algorithm for the prediction of water yield in hemispherical solar stills by Salwa Ahmad Sarow, Hasan Abbas Flayyih, Maryam Bazerkan, Luttfi A. Al-Haddad, Zainab T. Al-Sharify, Ahmed Ali Farhan Ogaili

“…The current work extends these experimental insights through XG-Boost to predict productivity, employing evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient of Variation of the Root Mean Squared Error (CVRMSE), and the determination coefficient (R2), with resulted values denoted as 0.43708%, 0.95879%, 0.2780%, 0.05290%, 12.2078%, and 0.88144% respectively. …”
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Advanced vehicle-to-grid control: enhancing energy exchange and power quality with grey wolf optimized bidirectional converters in EV charging infrastructure by Nagarajan Munusamy, Indragandhi Vairavasundaram

“…The proposed approach reduces average error reduction by 15%, grid current Total Harmonic Distortion (THD) by 20%, and DC link voltage surge during load transients from 12.5% to 2.3% compared to typical PI controllers. …”
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The Impact of Building Level of Detail Modelling Strategies: Insights into Building and Urban Energy Modelling by Daniel Bishop, Mahdi Mohkam, Baxter L. M. Williams, Wentao Wu, Larry Bellamy

“…Conversely, modest error reductions can be obtained via simple model improvements, such as the inclusion of eaves and window border shading. …”
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Effect of model’s skill level and frequency of feedback on learning of complex serial aiming task by Gh. Lotfi, F. Hatami, F. Zivari

“…Conclusion: According to Fitz’s speed-accuracy trade-off law, the results are justified as following: since the expert model observers focus on error reduction and increased accuracy in executing complex tasks, their movement time gets longer. …”
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Tether Force Estimation Airborne Kite Using Machine Learning Methods by Akarsh Gupta, Yashwant Kashyap, Panagiotis Kosmopoulos

“…Our XGBoost model, for example, demonstrated a notable reduction in error in predicting the tether force that can be extracted at a particular location, with a root mean square error of 52.3 Newtons and a mean absolute error of 32.1 Newtons, coupled with a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> error, which measures the proportion of variance explained by the model, achieved an impressive value of 0.93. …”
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THE SOLUTION TO THE EQUATION OF THE SHOCK WAVE BURGERS IN THE PTC MATHCAD ENVIRONMENT by R. E. Oleynikova

“…The system of equations of ordinary differential equations is integrated by means of the Euler computational scheme, and the step size is maintained small enough that the errors associated with numerical integration are negligible in comparison with the approximation error. …”
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Spatially and temporally correlated channel estimation and detection for comparator network-aided MIMO receivers with 1-bit ADCs by Luiz Sampaio, Lukas T. N. Landau

“…Abstract The low-resolution aware linear minimum mean squared error (LRA-LMMSE) channel estimator, designed for low-resolution MIMO receivers, achieves a notable reduction in mean squared error by incorporating a comparator network. …”
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Dynamic Interference Control in OFDM-Based Cognitive Radio Network Using Genetic Algorithm by Hamza Khan, Sang-Jo Yoo

“…The results show that the side lobes of the OFDM-based secondary user signal can be reduced by up to 38 dB and the PU interference tolerable limit can be satisfied at the cost of a minor addition in bit error rate (BER). The results further show that the proposed method delivers better performance as compared to non-GA additive signal method in terms of side lobe reduction as well as BER.…”
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Design and Analysis of Adaptive Fuzzy Super-Twisting Sliding Mode Controller for Uncertain 2-DOF Robotic Manipulator by Hayleyesus Girma Dirara, Feleke Tsegaye Yareshe, Chala Merga Abdissa

“…For link 1, the IAE is reduced to 0.01465 rad, representing a 99.9% reduction compared to SMC (14.24 rad), an 85% reduction compared to ST-SMC (0.0977 rad), and a 78.5% reduction compared to AF-SMC (0.06819 rad). …”
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Optical OTFS waveform PAPR analysis for high order modulation employing CNN, DNN, and AE machine learning algorithms under a variety of channel scenarios by Arun Kumar, Nishant Gaur, Aziz Nanthaamornphong

“…The simulations result prove that the proposed method attains a PAPR reduction of about 4 dB and 3.8 dB for 256-QAM and 2.2 dB and 1.6 dB for 64-QAM under a Rayleigh and Rician channel at a Complementary Cumulative Distribution Function (CCDF) of 10-5, better than conventional PAPR reduction methods. …”
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Calculation of Correction Factors for Vickers’ Hardness Measurements on a Non-Planar Surface by S. G. Sandomirski, A. L. Val’ko, S. P. Rudenko

“…Simplification of calculation of K coefficient and decrease of calculation error in comparison with the recommended in the regulatory documents obtaining of K value by linear interpolation relative to two adjacent table values are shown.The reduction of the calculation error in comparison with the calculation recommended in the regulatory documents occurred because of the reason that when calculating by the developed formulas, the error in the value of the calculated for a specific value of d/D coefficient K is averaged over all n values of d/D given in the table of GOST for a given surface. …”
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INVARIAN AUTOMATIC CONTROL SYSTEM, USING THE INTERMEDIATE-FREQUENCY SIGNALS OF HEAT POWER PARAMETERS by G. T. Kulakov, A. N. Kukhorenko, I. M. Golinko

“…And that is why it leads to the further reduction of maximal dynamic regulation error in processing of external disturbance by consumption of steam, and this allows to improve the quality of control.…”
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Adaptive Modulation Tracking for High-Precision Time-Delay Estimation in Multipath HF Channels by Qiwei Ji, Huabing Wu

“…Simulation results based on the Watterson channel model demonstrate that MATE achieves an average time-delay estimation error of approximately 0.01 ms with a standard deviation of approximately 0.01 ms, representing a 94.12% reduction in mean error and a 96.43% reduction in standard deviation compared to the traditional Generalized Cross-Correlation (GCC) method. …”
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0.003°/h bias instability of honeycomb disk resonator gyroscope achieved by mode reversal combined mode deflection control method by Liangqian Chen, Qingsong Li, Tongqiao Miao, Peng Wang, Xuhui Zhang, Yang Zhang, Xuezhong Wu, Dingbang Xiao

“…This paper incorporates electrode machining error and capacitance detection nonlinear error into the gyroscope model, resulting in a more comprehensive bias output model. …”
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Introducing an Evolutionary Method to Create the Bounds of Artificial Neural Networks by Ioannis G. Tsoulos, Vasileios Charilogis, Dimitrios Tsalikakis

“…The new method effectively constructs the parameter value range of the artificial neural network with one processing level and sigmoid outputs, both achieving a reduction in training error and preventing the network from experiencing overfitting phenomena. …”
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Implementation of an Active Vibration Control System on a Cogeneration Plant by Emanuele Voltolini, Daniel Pinardi, Andrea Toscani, Marco Binelli, Angelo Farina, Jessica Ferrari, Andrea Zenaro, Stefano Maglia, Enrico Calzavacca

“…Usually, in these applications the control devices are loudspeakers, and the error signals come from microphones. In outdoor applications, this solution comes with some limitations, such as the ageing of the loudspeakers due to humidity, temperature, rain and dust, and the presence of wired connections between the error sensors and the control system. …”
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Enhanced Signal-to-Noise Ratio Estimation in Optical Fiber Communications: A Pilot-Based Approach by Mohamed Al-Nahhal, Ibrahim Al-Nahhal, Sunish Kumar Orappanpara Soman, Octavia A. Dobre

“…The estimation accuracy of the SNR components achieved by the proposed estimators is evaluated using the normalized root mean square error and the standard deviation of the estimation errors. …”
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