MINIMIZATION OF RISK OF THE ERRONEOUS DECISION IN THE ASSESSMENT OF THE IMPORTANCE OF STATISTICAL RELATIONS OF TECHNICAL AND ECONOMIC INDICATORS OF THE OBJECTS OF ELECTRIC POWER SY... by E. M. Farhadzadeh, A. Z. Muradaliyev, Yu. Z. Farzaliyev, T. K. Rafiyeva, S. A. Abdullayeva

“…Traditionally, increase of reliability of the decision is reached by reduction of a Type I error. Usually it is accepted to be equal to 5%, occasionally – to 1%, and at researches – even to 0.5 %. …”
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Temporal dependent rate-distortion optimization based on distortion backward propagation by Hongwei GUO, Ce ZHU, Xu YANG, Lei LUO

“…Rate-distortion optimization (RDO) is a crucial technique in block based hybrid video encoders.However, the widely used independent RDO is far from obtaining optimal coding performance.To improve the rate-distortion (R-D) performance of high efficiency video coding (HEVC), a temporal dependent RDO algorithm was proposed.Firstly, the formula to calculate temporal distortion propagation factor was derived by using an exponential R-D function.Then, the coding distortion and motion compensation predicted error were obtained by pre-encoding, and the temporal distortion propagation factor was estimated by using distortion backward propagation.Finally, the Lagrange multiplier and quantization parameter of coding tree unit were adaptively adjusted to optimize bit resources allocation.Experimental results show that compared with the original RDO method in HEVC under the low-delay configuration, the proposed algorithm achieves an average 4.4% bit rate reduction for all test sequences, and up to 13.0% bit rate reduction for test sequence BasketballDrill, at the same reconstructed video quality.…”
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Channel estimation method of massive MIMO-OFDM system based on adaptive compressed sensing by Yiyang HU, Lina QI

“…Massive multiple-input multiple-output (MIMO) is a solution for efficiently providing connection services for a variety of machine equipment in the Internet of things (IoT), and efficient connection services require accurate channel estimation.Aimed at the problems of high pilot overhead and poor performance of normalized mean square error (NMSE) estimation in downlink channel estimation of massive MIMO systems, based on the compressed sensing (CS) theory, the common sparsity of the channel space domain was combined while using the feature of lower sparsity of adjacent time slot differential channel impulse response (CIR), which leaded to a significant reduction in pilot overhead.In the reconstruction algorithm, a two-stage differential estimation algorithm, which divided the channel estimation in consecutive time slots with time correlation into two stages, was proposed and the idea of adaptive compressed sensing was combined to achieve fast and accurate CIR estimate.The simulation results show that the proposed two-stage differential channel estimation algorithm not only has a significant improvement in the estimated NMSE performance and data transmission rate compared to the existing CS-based multiple measurement vector (MMV) algorithm, but also show a certain reduction in runtime complexity.…”
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Temporal dependent rate-distortion optimization based on distortion backward propagation by Hongwei GUO, Ce ZHU, Xu YANG, Lei LUO

“…Rate-distortion optimization (RDO) is a crucial technique in block based hybrid video encoders.However, the widely used independent RDO is far from obtaining optimal coding performance.To improve the rate-distortion (R-D) performance of high efficiency video coding (HEVC), a temporal dependent RDO algorithm was proposed.Firstly, the formula to calculate temporal distortion propagation factor was derived by using an exponential R-D function.Then, the coding distortion and motion compensation predicted error were obtained by pre-encoding, and the temporal distortion propagation factor was estimated by using distortion backward propagation.Finally, the Lagrange multiplier and quantization parameter of coding tree unit were adaptively adjusted to optimize bit resources allocation.Experimental results show that compared with the original RDO method in HEVC under the low-delay configuration, the proposed algorithm achieves an average 4.4% bit rate reduction for all test sequences, and up to 13.0% bit rate reduction for test sequence BasketballDrill, at the same reconstructed video quality.…”
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Multi-Task Trajectory Prediction Using a Vehicle-Lane Disentangled Conditional Variational Autoencoder by Haoyang Chen, Na Li, Hangguan Shan, Eryun Liu, Zhiyu Xiang

“…Extensive evaluations on the nuScenes dataset demonstrate the effectiveness of MS-SLV, achieving a 12.37% reduction in average displacement error and a 7.67% reduction in final displacement error over state-of-the-art methods. …”
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Improving of the Generation Accuracy Forecasting of Photovoltaic Plants Based on <i>k</i>-Means and <i>k</i>-Nearest Neighbors Algorithms by P. V. Matrenin, A. I. Khalyasmaa, V. V. Gamaley, S. A. Eroshenko, N. A. Papkova, D. A. Sekatski, Y. V. Potachits

“…For the best case, the error in forecasting of a photovoltaic plant generation an hour ahead was 9 %.…”
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Deep learning-based dual monitoring system for power forecasting and fault detection in nuclear power applications by Mingzhe Lyu, Helin Gong, Zhang Chen, Jiangyu Wang, Mingxiao Zhong, Zhiyong Wang, Qing Li, Zefei Pan

“…In the most informative case, the model achieves a 56.6% reduction in root mean square error and a 36.8% reduction in mean absolute error, with a coefficient of determination (R2) of 0.9924—significantly outperforming the next-best benchmark. …”
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齿轮修形参数对变速箱传动特性影响的研究 by 杨本洋, 褚超美, 汤海川, 缪国

“…Focusing on the vibration and noise of gear transmission,taking Masta software as the analysis platform,by simulating and computing the modification of main reduction gear of a transmission,the effect relations between change of modification parameters such as tip relief,barreling relief and crowning relief with characteristic of gear transmission is obtained.The optimum modification parameters are selected based on the study of the law of gear transmission error and max contact pressure.Noise test result shows that the squeal of the transmission is reduced effectively after gear modification.…”
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基于小波包分析和改进自适应遗传算法的齿轮故障诊断 by 戚晓利, 潘紫微

“…The gear is one of the most important elements in the trainsmission system.Aiming at the fault diagnosis problem of gear,a new gear fault diagnosis method that based on wavelet packet analysis and improving adaptive genetic algorithm is put forward,and which is on the basis of synthesizing the advantages of wavelet-packet noise reduction,fuzzy logic,high-order BP neural network and improving adaptive genetic algorithm.The experimental results show that this method has advantage over the routine method in classification precision and training total error control.…”
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Causality-Driven Feature Selection for Calibrating Low-Cost Airborne Particulate Sensors Using Machine Learning by Vinu Sooriyaarachchi, David J. Lary, Lakitha O. H. Wijeratne, John Waczak

“…Similarly, for the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>PM</mi><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula> model, the proposed feature selection led to a 33.2% reduction in the mean squared error, outperforming the 30.2% reduction achieved by the SHAP value-based selection. …”
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Comparison of Three Supervised Learning Methods for Digital Soil Mapping: Application to a Complex Terrain in the Ecuadorian Andes by Martin Hitziger, Mareike Ließ

“…The median reduction of the root mean square error is around 5%. …”
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Two-steps variance calibrated estimators with linear and non-linear constraints for mailed surveys with non-response by Ahmed Audu, Maggie Aphane

“…In the second step, the constants of proportionality are determined based on different objectives, such as bias reduction or minimum mean squared error. This paper thoroughly examined the theoretical and numerical properties of the proposed estimators. …”
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On Bayesian estimation of a latent trait model defined by a rank-based likelihood by Daniel Biftu Bekalo, Anthony Kibira Wanjoya, Samuel Musili Mwalili

“…Performance metrics like mean absolute error, root mean square error, and 95% confidence interval coverage probability revealed the estimates from the proposed Bayesian method surpassed those from classical approaches. …”
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THE PROFILE OF CREATIVE THINKING PROCESS: PROSPECTIVE MATHEMATICS TEACHERS by Gunawan Gunawan, Ferry Ferdianto, Fauzi Mulyatna, Reni Untarti

“…The term to describe the estimation stage is "trial and error." Significance –The findings show that the estimation stage, or "trial and error," helps accelerate the acquisition of creative ideas. …”
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Enhanced OCR Recognition for Madurese Text Documents: A Genetic Algorithm Approach with Tesseract 5.5 by Muhammad Nazir Arifin, Muhammad Umar Mansyur, Ali Rahman, Nindian Puspa Dewi, Fauzan Prasetyo Eka Putra

“…The GA-optimized preprocessing sequence achieved a 24.32% Word Error Rate (WER) and 7.47% Character Error Rate (CER), marking substantial improvements over the baseline Tesseract implementation. …”
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Research on the application of improved MCMC algorithm in the measurement of high-dimensional financial data by Naijia Liu, Yuchao Su

“…In the empirical experiments, the estimation error of the fluctuation parameters of the improved algorithm is only 0.0018; the estimation error of the noise variance is only 0.0011; the annealing time is 342 s; and the iteration time is 364 s. …”
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Reconstruction of Temperature Distribution by Acoustic Tomography Based on Principal Component Analysis and Deep Neural Network by ZHANG Lifeng, LI Jing, WANG Zhi

“…In addition, the average relative error and root mean square error of reconstructed image are less than 0.36% and 0.85% respectively.…”
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ARIMA-Kriging and GWO-BiLSTM Multi-Model Coupling in Greenhouse Temperature Prediction by Wei Zhou, Shuo Liu, Junxian Guo, Na Liu, Zhenglin Li, Chang Xie

“…Across different prediction horizons (10 min and 30 min intervals), the GWO-BiLSTM model demonstrated superior performance with key metrics reaching a coefficient of determination (R²) of 0.97, root mean square error (RMSE) of 0.79–0.89 °C (41.7% reduction compared to the PSO-BP model), mean absolute percentage error (MAPE) of 4.94–8.5%, mean squared error (MSE) of 0.63–0.68 °C, and mean absolute error (MAE) of 0.62–0.65 °C, significantly outperforming the BiLSTM, LSTM, and PSO-BP models. …”
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Simulation of Ground Visibility Based on Atmospheric Boundary Layer Data Using K-Nearest Neighbors and Ensemble Model Algorithms by Ruolan Liu, Shujie Yuan, Duanyang Liu, Lin Han, Fan Zu, Hong Wu, Hongbin Wang

“…In the second fog event, the addition of atmospheric pollutant concentration data from the boundary layer further improved results (ensemble model: for VIS < 200 m, Schemes 2 and 3 had MAEs of 20.1 m, corresponding to a relative error of less than 10.1%, and 11.4 m, corresponding to a relative error of less than 5.7%, respectively). …”
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Investigating the accuracy of adjusting for examiner differences in multi-centre Objective Structured Clinical Exams (OSCEs). A simulation study of video-based Examiner Score Compa... by Peter Yeates, Gareth McCray

“…We replicated this 1000 times for each permutation to determine average error reduction and the proportion of students whose scores became more accurate. …”
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