Performance Prediction Method Research Articles

Security Performance Prediction Method of Artificial Intelligence of Things Based on Lightweight MS-Net Network

Security Performance Prediction Method of Artificial Intelligence of Things Based on Lightweight MS-Net Network

Research on learning and evaluation model of traditional sports skills of ethnic minorities using optimized spiking deep residual network

The athletic performance of competitors in sporting events is influenced by a range of parameters. It is challenging for coaches to develop specialized, scientific training programs for athletes’ problems because the standard sports performance prediction approach makes it impossible to obtain precise findings and the related data analysis promptly. A sports performance prediction using the Spiking Deep Residual Network based Coati Optimization Algorithm (SPP-SDRN-COA) is proposed in this manuscript. Initially, the input data was collected from the historical data of ethnic minority athletics. Afterward, data was fed to pre-processing. In pre-processing, the Domain Transform Filtering (DTF) method removed noise by using DTF. Next, the pre-processed data was given to the Spiking Deep Residual Network (SDRN) which classified the sports performance of an athlete’s strengths and weakness. In general, SDRN does not show any optimization adaption methods to determine the optimum parameter to offer an accurate prediction. The Coati Optimization Algorithm (COA) is proposed in this manuscript to optimize the SDRN classifier that detects the strengths and weaknesses of the sports performance in an accurate manner. The proposed SPP – SDRN – COA is implemented using the MATLAB platform. By using the proposed SPP – SDRN – COA based sports performance prediction, the method provides higher accuracy and better prediction. Additionally, coaches can analyze athletic performance and gain a more thorough understanding of athletes’ strength levels than using the conventional performance prediction method.

Motion performance prediction of underwater gliders based on deep learning and image modeling

With the industrialization development and pedigree application, the underwater gliders are facing more extensive motion modes and application scenarios. However, it is complicated to establish a specific relationship between the explicit motion property and the implicit design parameters. This study proposes an accurate and rapid performance prediction method for underwater gliders based on deep learning and image modeling. First of all, a parametric model is established to generate large quantities of data about design parameters and performance indexes. Then, an image modeling method is employed to transform the numerical data of the glider body into structure images. Finally, a hybrid convolutional neural network-multilayer perceptron (CNN-MLP) model is proposed, and the structure images and other numerical data are input into the model to predict the total gliding range of the gliders. The results show that the developed hybrid CNN-MLP model achieves excellent prediction ability. Sea trial data further demonstrate the effectiveness of the proposed model. Thus, the proposed architecture provides a promising method for predicting underwater glider performance with multidimensional data and can be further applied to other types of gliders. Our research also provides a valuable reference for intelligent design and optimization iteration of other underwater equipment.

A Method for Prediction and Analysis of Student Performance That Combines Multi-Dimensional Features of Time and Space

The prediction and analysis of students’ academic performance are essential tools for educators and learners to improve teaching and learning methods. Effective predictive methods assist learners in targeted studying based on forecast results, while effective analytical methods help educators design appropriate educational content. However, in actual educational environments, factors influencing student performance are multidimensional across both temporal and spatial dimensions. Therefore, a student performance prediction and analysis method incorporating multidimensional spatiotemporal features has been proposed in this study. Due to the complexity and nonlinearity of learning behaviors in the educational process, predicting students’ academic performance effectively is challenging. Nevertheless, machine learning algorithms possess significant advantages in handling data complexity and nonlinearity. Initially, a multidimensional spatiotemporal feature dataset was constructed by combining three categories of features: students’ basic information, performance at various stages of the semester, and educational indicators from their places of origin (considering both temporal aspects, i.e., performance at various stages of the semester, and spatial aspects, i.e., educational indicators from their places of origin). Subsequently, six machine learning models were trained using this dataset to predict student performance, and experimental results confirmed their accuracy. Furthermore, SHAP analysis was utilized to extract factors significantly impacting the experimental outcomes. Subsequently, this study conducted data ablation experiments, the results of which proved the rationality of the feature selection in this study. Finally, this study proposed a feasible solution for guiding teaching strategies by integrating spatiotemporal multi-dimensional features in the analysis of student performance prediction in actual teaching processes.

A novel performance prediction method for harmonic reducers based on the trend-enhanced Fisher’s discriminant ratio-Wiener process

Abstract Harmonic reducers, as core components of industrial robots, play a critical role in maintaining robot health. Performance degradation assessment and remaining useful life (RUL) prediction are essential for ensuring the operational reliability of such robots. To address the multistage characteristics and stochastic uncertainties in the performance degradation of harmonic reducers, a performance prediction method based on Fisher’s discriminant ratio and Wiener process is proposed. Firstly, the health indicator construction is defined as an optimization problem based on Fisher’s discriminant ratio and trend monotonicity constraints. By leveraging the sum of the multiplication of frequency amplitudes and optimized weights, precise segmentation of the multistage degradation process over the entire lifecycle is achieved. Notably, the optimized weights can automatically identify the resonance frequency bands caused by damage. Subsequently, a nonlinear Wiener process model with a drift coefficient in the form of a power function is established. The probability density function expression for RUL is derived based on the concept of first hit time. Additionally, a logarithmic likelihood function for the unknown parameters in the degradation model is constructed. The experimental results indicate that the proposed method surpasses the other two Wiener process models in terms of root mean square error, mean absolute percentage error, and cumulative relative accuracy. This provides robust support for preventive maintenance decision-making for harmonic reducers.

Multi-span optical power spectrum prediction using cascaded learning with one-shot end-to-end measurement

Scalable methods for optical transmission performance prediction using machine learning (ML) are studied in metro reconfigurable optical add-drop multiplexer (ROADM) networks. A cascaded learning framework is introduced to encompass the use of cascaded component models for end-to-end (E2E) optical path prediction augmented with different combinations of E2E performance data and models. Additional E2E optical path data and models are used to reduce the prediction error accumulation in the cascade. Off-line training (pre-trained prior to deployment) and transfer learning are used for component-level erbium-doped fiber amplifier (EDFA) gain models to ensure scalability. Considering channel power prediction, we show that the data collection process of the pre-trained EDFA model can be reduced to only 5% of the original training set using transfer learning. We evaluate the proposed method under three different topologies with field deployed fibers and achieve a mean absolute error of 0.16 dB with a single (one-shot) E2E measurement on the deployed 6-span system with 12 EDFAs.

An Explainable Student Performance Prediction Method Based on Dual-Level Progressive Classification Belief Rule Base

Explainable artificial intelligence (XAI) is crucial in education for making educational technologies more transparent and trustworthy. In the domain of student performance prediction, both the results and the processes need to be recognized by experts, making the requirement for explainability very high. The belief rule base (BRB) is a hybrid-driven method for modeling complex systems that integrates expert knowledge with transparent reasoning processes, thus providing good explainability. However, class imbalances in student grades often lead models to ignore minority samples, resulting in inaccurate assessments. Additionally, BRB models face the challenge of losing explainability during the optimization process. Therefore, an explainable student performance prediction method based on dual-level progressive classification BRB (DLBRB-i) has been proposed. Principal component regression (PCR) is used to select key features, and models are constructed based on selected metrics. The BRB’s first layer classifies data broadly, while the second layer refines these classifications for accuracy. By incorporating explainability constraints into the population-based covariance matrix adaptation evolution strategy (P-CMA-ES) optimization process, the explainability of the model is ensured effectively. Finally, empirical analysis using real datasets validates the diagnostic accuracy and explainability of the DLBRB-i model.

Flexural performance and crack width prediction of steel-UHTCC composite bridge decks with wet joints

Composite bridge deck (CBD) structure has been widely used in recent decades, particularly in long-span bridges. The application of ultra-high toughness cementitious composite (UHTCC) in composite bridge deck (CBD) structures has garnered attention due to its exceptional resistance to tensile cracking and strain hardening. However, their widespread adoption necessitates a thorough understanding of crack resistance, especially in the presence of wet joints between cast regions. This study investigates the flexural performance and crack width prediction method of steel-UHTCC/concrete CBDs with wet joints using a combined experimental and theoretical approach. Six specimens with UHTCC wet joints and either UHTCC or concrete precast layers were designed and tested under hogging moment. The analysis focused on failure modes, load-deflection responses, and crack development while considering the wet joints. All specimens exhibited interfacial slip between the UHTCC/concrete layer and the steel beam. A novel theoretical model was developed to predict crack width in steel-UHTCC/concrete CBDs with wet joints. This model incorporates established crack theories for fiber-reinforced concrete while explicitly accounting for the strain-hardening behavior of UHTCC. Furthermore, this model provides a detailed bending analysis of steel-UHTCC CBDs considering the interface slip phenomenon, to derive various mechanical parameters for crack width prediction.

Prediction of mechanical properties of manufactured sand polymer-modified mortar based on swarm intelligence algorithm

Because polymer-modified mortar (PMM) exhibits a complex and diverse composition, there is a complex nonlinear relationship between its mechanical properties and mix proportions that is challenging for conventional mechanical performance prediction methods to accurately predict. Consequently, in this study, a predictive model utilizing a backpropagation neural network (BPNN) was formulated, featuring a structure of 6–14-2. Simultaneously, three swarm intelligence algorithms were integrated—the ant colony optimization algorithm, grey wolf optimization algorithm, and bat optimization algorithm (BAT)—to collectively refine the optimization process of the BPNN prediction model. The model's input layer included cement (OPC), cellulose ether (CE), dispersible polymer powder (DPP), antifoam (AF), fly ash (FA), and tuff stone powder (SP), and the output layer consisted of the compressive and bond strengths. The model dataset comprised 520 samples (260 × 2), with 60 % (312) used for model establishment and 40 % (208) used for validation. Correlation matrix and principal component analyses were conducted on the dataset, along with a comparative analysis of the factors influencing the mechanical performance evaluation indicators. The results indicate that at 7 and 28 d, there was a positive correlation between the DPP and AF with the development of PMM mechanical properties. At 7 d, the SP and FA were negatively correlated with the compressive and flexural strength, whereas the CE was positively correlated with the bond strength. At 28 d, the OPC was negatively correlated with the compressive, bond, and flexural strengths, and positively correlated with the SP and FA. C3 represents the optimal mix proportion for the PMM, and considering the influence of all raw materials, F3 was identified as the comprehensive optimal mix proportion. The predictive performance evaluation indicators of the BAT–BPNN for the compressive and bond strengths were R2 = 0.980 and 0.942, MAE = 5.967 and 1.958, MAPE = 0.071 and 0.041, and RMSE = 4.686 and 1.594, respectively. BAT significantly improves the predictive accuracy of the BPNN model, enabling the prediction of PMM mechanical properties and providing a scientific basis for its mix proportion design.

Comparative analysis of feature selection and extraction methods for student performance prediction across different machine learning models

Education is at the core of developmental progress, necessitating the exploration and implementation of diverse contemporary methods to ensure the success of students across multiple levels. However, impediments to this success exist, categorized into three primary groups which are, individual factors, family factors and social factors. These factors can manifest as absenteeism and boredom, posing a threat to the future of both students and society at large. Addressing these challenges proves to be a complex task for educators and pedagogues, given the unique problems each student faces. This paper aims to employ a broad spectrum of feature selection and extraction methods, some unconventional in the education sector but proven reliable in other domains. By integrating machine learning (ML) and deep learning (DL) models, we seek to predict student performance based on these identified factors. Subsequently, a comparative analysis will be conducted to determine the most effective model, considering the relevance of various factors.

Equation-Oriented Meanline Method for Axial Turbine Performance Prediction Under Choking Conditions

Abstract Meanline models play a crucial role in turbine design and system-level analyses, facilitating rapid evaluation of design concepts and prediction of off-design performance. Most of the existing meanline methods are inadequate in predicting turbine performance under choking conditions. These models either neglect the impact of losses on choking, or increases the computational complexity significantly. This limitation is addressed in this work, presenting a novel meanline model. The choking state at each cascade is determined by maximizing the mass flow rate, while taking into account the effect of losses. Leveraging the method of Lagrange multipliers, the optimization problems are transformed into a set of equations that seamlessly integrate with the rest of the meanline model. The resulting system of equations is then solved simultaneously using efficient root-finding algorithms, resulting in fast and reliable convergence. Validation against experimental data from three different turbines demonstrates the model's ability to accurately predict mass flow rate, torque, and exit flow angles across single- and multi-stage turbines, with errors typically within ±2.5 % and ±5.0 % for mass flow rate and torque respectively, and within ±5 degrees for flow angles. The proposed approach represents a significant advancement in meanline modeling, offering improved accuracy and computational efficiency.

A New Continuous Strength Method for Prediction of Strain-Hardening Performance of High-Strength Aluminum Alloy Cylindrical Columns

This paper aims to develop a new continuous strength method (CSM) to more accurately predict the strain-hardening characteristics of high-strength aluminum alloy circular hollow sections (CHSs) under axial compression. A total of 11 stub column specimens made of 7A04-T6 and 6061-T6 aluminum alloys underwent testing. Additionally, 16 sets of experiment data were gathered from open sources, encompassing various aluminum alloy types such as 6082-T6, 6061-T6, and 6063-T5. Validated by experimental result, the finite element (FE) model was applied in a series of comprehensive parameter studies, supplementing the limited test result of high-strength aluminum alloy stub columns. Based on the experiment and FE results, this paper proposes a new CSM relation to determine the cross-sectional resistance of high-strength non-slender CHS aluminum alloys under compression. The cross-sectional resistance obtained from tests are compared with predicted strengths determined using the European code, as well as the solution of the CSM proposed in a previous study and in this paper. The comparison illustrates that the strength predictions in the European code and the previous study are conservative. Compared with the European code and the previous study, the strength prediction formula proposed in this paper improves accuracy by 11% and 5%, respectively, while reducing scatter by 8.4% and 2%, respectively.

Exploring explainable ensemble machine learning methods for long-term performance prediction of industrial gas turbines: A comparative analysis

In today's modern life, where electricity demand is one of the fundamental necessities, gas turbines play a pivotal role in meeting this demand. As such, it is imperative to address the challenges faced in the field. Current models often rely on simplifying assumptions, neglecting the intricate relationships between variables. This limitation leads to reduced accuracy and reliability, ultimately affecting the overall efficiency of gas turbine systems. Furthermore, the complexity of gas turbine behavior, coupled with the scarcity of comprehensive datasets, exacerbates the problem.To address these challenges, this research aimed to develop an advanced model capable of accurately forecasting real gas turbine behavior. The proposed approach leveraged ensemble decision trees, robust preprocessing techniques, and rigorous evaluation using an extensive dataset spanning from 2011 to 2015. The training and validation phases were conducted on data from 2011 to 2014, with the 2015 dataset reserved for evaluation.The results demonstrated that the bagging structure outperformed the boosted structure, exhibiting lower complexity and higher reliability. Remarkably, the bagging approach with only 30 estimators achieved a superior root mean square error of 1.4176, outperforming the boosted trees with 200 learners. The model effectively captured the overall gas turbine performance, though it encountered limitations in certain specific operating ranges.To further investigate the model's behavior, an evaluation was conducted to assess the effects of the input variables on the output power. While the interpretability of the results posed some challenges, the overall findings were deemed acceptable and provide valuable insights for optimizing gas turbine performance. The significance of this research lies in its potential to inform decision-making and enhance the efficiency of gas turbine systems, ultimately contributing to the reliable and sustainable supply of electricity.

Enhanced Calibration and Performance Prediction Method for Entire Propulsion System of eVTOL UAV

Enhanced Calibration and Performance Prediction Method for Entire Propulsion System of eVTOL UAV

A New Student Performance Prediction Method Based on Belief Rule Base with Automated Construction

Student performance prediction (SPP) is a pivotal task in educational analytics, enabling proactive interventions and optimized resource allocation by educators. Traditional SPP models are often hindered by their complexity and lack of interpretability. This study introduces a novel SPP framework, the Belief Rule Base with automated construction (Auto–BRB), designed to address these issues. Firstly, reference values are derived through data mining techniques. The model employs an IF–THEN rule-based system integrated with evidential reasoning to ensure both transparency and interpretability. Secondly, parameter optimization is achieved using the Projected Covariance Matrix Adaptive Evolution Strategy (P–CMA–ES), significantly enhancing model accuracy. Moreover, the Akaike Information Criterion (AIC) is then applied to fine-tune the balance between model accuracy and complexity. Finally, case studies on SPP have shown that the Auto–BRB model has an advantage over traditional models in terms of accuracy, while maintaining good interpretability. Therefore, Auto–BRB has excellent application effects in educational data analysis.

Performance prediction method of solid rocket motor based on the ground test

Abstract Solid rocket motor (SRM) is a core component of rockets. Considering the high cost of solid rocket motors and the high demand for transportation and storage, there is an urgent need to predict the performance of solid rocket motors before the ground ignition test to lower the cost of tests. Using data mining technology to predict various parameters of rocket motors with historical data has become a new development direction. In order to improve the data fitting effect and prediction accuracy, a novel prediction model was proposed, which tries to combine the Whale Optimization Algorithm (WOA) with the Error Correction Gated Recurrent Unit neural network (ECGRU) to correct the basic prediction performance of the Long Short-Term Memory neural network model (LSTM). Compared with traditional methods, experiments showed that the novel model was beneficial in improving the reliability of the prediction of the thrust and specific impulse.

Prediction of steady hydrodynamic performance of pump jet propulsion based on free wake vortex model

Due to mutual interference between various components, the transitional motion of fluid particles in the flow field of pump jet propulsion becomes more complex and variable. Additionally, the vortex structure at the wake can undergo severe contraction and deformation. To further improve the accuracy of predicting the hydrodynamic performance of pump jet propulsion, this paper proposes a time-varying, space-varying, and induced velocity-varying free wake numerical calculation model based on potential flow theory. A set of hydrodynamic performance prediction methods that consider the interference of the free wake model is also established. Furthermore, a velocity smoothing model based on time reversal is constructed based on the distribution pattern of numerical singularity points during the induced velocity calculation process, which enhances the robustness of the calculation program. By comparing and analyzing the numerical calculation results with experimental results, including pressure, circulation distribution, and hydrodynamic performance, the accuracy of the free wake model proposed in this paper is verified. The results also demonstrate that the free wake model proposed in this paper can effectively improve the prediction accuracy of the hydrodynamic performance of the pump jet propulsion.

Degradation performance rapid prediction and multi-objective operation optimization of gas turbine blades

The performance degradation of high-temperature blades will affect the safety and efficiency of operation. In response to issues such as ignoring strength characteristics, slow degradation performance prediction, insufficient applicability and accuracy of traditional proxy models, and long operation optimization cycle, a degradation performance prediction model of turbine blades based on the back propagation neural network is established in this research. Furthermore, the efficiency-stress multi-objective operation optimization method based on genetic algorithm is proposed. The results show that the R-square value of the degradation performance neural network prediction model is greater than 0.9 for predicting characteristic parameters. The prediction speed is improved by 104 orders of magnitude compared to the traditional computational fluid dynamics method. The influence mechanisms of turbine outlet flow rate, cooling gas inlet total temperature, and cooling gas flow rate on the degradation performance are revealed. Under fouling extreme conditions and corrosion/wear extreme conditions, the operating parameters of turbine blades are optimized. The maximum values of the efficiency improvement, power increase and maximum von Mises stress reduction are 0.74 %, 2.13 MW and 43 MPa, respectively. This study can provide an effective and novel method for real-time degradation performance prediction and rapid operating condition optimization of turbine blades.

X-Ray Image Generation as a Method of Performance Prediction for Real-Time Inspection: a Case Study

X-Ray Image Generation as a Method of Performance Prediction for Real-Time Inspection: a Case Study

Experimental and Numerical Investigation Integrated with Machine Learning (ML) for the Prediction Strategy of DP590/CFRP Composite Laminates.

This study unveils a machine learning (ML)-assisted framework designed to optimize the stacking sequence and orientation of carbon fiber-reinforced polymer (CFRP)/metal composite laminates, aiming to enhance their mechanical properties under quasi-static loading conditions. This work pioneers the expansion of initial datasets for ML analysis in the field by uniquely integrating the experimental results with finite element simulations. Nine ML models, including XGBoost and gradient boosting, were assessed for their precision in predicting tensile and bending strengths. The findings reveal that the XGBoost and gradient boosting models excel in tensile strength prediction due to their low error rates and high interpretability. In contrast, the decision trees, K-nearest neighbors (KNN), and random forest models show the highest accuracy in bending strength predictions. Tree-based models demonstrated exceptional performance across various metrics, notably for CFRP/DP590 laminates. Additionally, this study investigates the impact of layup sequences on mechanical properties, employing an innovative combination of ML, numerical, and experimental approaches. The novelty of this study lies in the first-time application of these ML models to the performance optimization of CFRP/metal composites and in providing a novel perspective through the comprehensive integration of experimental, numerical, and ML methods for composite material design and performance prediction.