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This study aimed to address these limitations by employing a systematic approach. Initially, body measurements of 115 children were meticulously recorded and subjected to statistical analysis. Subsequently, a children's jeans paper pattern was chosen, and a predictive model for pattern sizing was developed using BP neural networks. Additionally, a parametric mathematical model was constructed by integrating the design principles of the paper pattern. Using the Visual-LISP programming language on the Auto-CAD 2020 edition platform, an editing process was performed to create a children's jeans automatic drawing program. The predicted pattern size data for children's jeans were then imported into the DCL dialog box of Auto-CAD, enabling the automatic generation of paper patterns. To evaluate the effectiveness of the approach, the generated garment paper patterns were imported into the CLO-3D virtual fitting software. [ABSTRACT FROM AUTHOR]

With the rapid development of social economy, the demand for electric power engineering is gradually increasing. The power supply system is constantly developing in the direction of large space and automation, and various high and new technologies are also constantly improving. The power monitoring data access control system is used to monitor and control the power production and supply process and improve the power supply efficiency. The further development of the region also has a higher demand for power and energy supply. For the problem that the natural environment of transmission and distribution lines in various power grids is uncertain, which makes the line operation unsafe. This paper proposed a power monitoring data access control system based on BP (back propagation, abbreviated as BP) neural network. This paper described the related concepts of BP neural network and power monitoring system, and described the functions and construction methods of power monitoring data access control system. On this basis, relevant experiments were carried out to verify the performance of the proposed system. The experimental results showed that the fault detection accuracy of the traditional algorithm was about 93 %, while the fault detection accuracy of the algorithm in this paper was more than 98 %. The highest accuracy rate was 99.88 %, and the accuracy rate of fault detection was greatly improved. [ABSTRACT FROM AUTHOR]

Cutting parameters have a significant impact on the machining effect. In order to reduce the machining time and improve the machining quality, this paper proposes an optimization algorithm based on Bp neural networkImproved Multi-Objective Particle Swarm (Bp-DWMOPSO). Firstly, this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm. Secondly, the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established. Finally, the Bp-DWMOPSO algorithm is designed based on the established models. In order to verify the effectiveness of the algorithm, this paper obtains the required data through equal probability orthogonal experiments on a typical Computer Numerical Control (CNC) turning machining case and uses the Bp-DWMOPSO algorithm for optimization. The experimental results show that the Cutting speed is 69.4 mm/min, the Feed speed is 0.05 mm/r, and the Depth of cut is 0.5 mm. The results show that the Bp-DWMOPSO algorithm can find the cutting parameters with a higher material removal rate and lower spindle load while ensuring the machining quality. This method provides a new idea for the optimization of turning machining parameters. [ABSTRACT FROM AUTHOR]

The prediction of the ultimate bearing capacity (UBC) of composite foundations represents a critical application of test monitoring data within the field of intelligent geotechnical engineering. This paper introduces an effective combinational prediction algorithm, namely SA-IRMO-BP. By integrating the Improved Radial Movement Optimization (IRMO) algorithm with the simulated annealing (SA) algorithm, we develop a meta-heuristic optimization algorithm (SA-IRMO) to optimize the built-in weights and thresholds of backpropagation neural networks (BPNN). Leveraging this integrated prediction algorithm, we forecast the UBC of soil–cement mixed (SCM) pile composite foundations, yielding the following performance metrics: RMSE = 3.4626, MAE = 2.2712, R = 0.9978, VAF = 99.4339. These metrics substantiate the superior predictive performance of the proposed model. Furthermore, we utilize two distinct datasets to validate the generalizability of the prediction model presented herein, which carries significant implications for the safety and stability of civil engineering projects. [ABSTRACT FROM AUTHOR]

With the rapid development of China's aviation industry, the accurate prediction of civil aviation passenger volume is crucial to the sustainable development of the industry. However, the current prediction of civil aviation passenger traffic has not yet reached the ideal accuracy, so it is particularly important to improve the accuracy of prediction. This paper explores and compares the effectiveness of the backpropagation (BP) neural network model and the SARIMA model in predicting civil aviation passenger traffic. Firstly, this study utilizes data from 2006 to 2019, applies these two models separately to forecast civil aviation passenger traffic in 2019, and combines the two models to forecast the same period. Through comparing the mean relative error (MRE), mean square error (MSE), and root mean square error (RMSE), the prediction accuracies of the two single models and the combined model are evaluated, and the best prediction method is determined. Subsequently, using the data from 2006 to 2019, the optimal method is applied to forecast the civil aviation passenger traffic from 2020 to 2023. Finally, this paper compares the epidemic's impact on civil aviation passenger traffic with the actual data. This paper improves the prediction accuracy of civil aviation passenger volume, and the research results have practical significance for understanding and evaluating the impact of the epidemic on the aviation industry. [ABSTRACT FROM AUTHOR]

Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University, the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation (BP) neural network to solve nonlinear problems and have the ability of global approximation and generalization. By analyzing the influence of different uses, different building surfaces and different energy-saving schemes on the change of building energy consumption, the grey correlation method is used to determine the main influencing factors affecting each building energy consumption, including uses, building surfaces and energy-saving schemes, which are used as the input of the model and the building energy consumption as the output of the model, so as to establish the building energy consumption analysis model based on BP neural network. However, in practical application, BP neural network has the defects of slow convergence and easy to fall into local minima. In view of this, this paper uses genetic algorithm to optimize the weight and threshold of BP neural network, completes the improvement of various building energy consumption analysis models, and realizes the qualitative analysis of building energy consumption. The model verification results show that the viscosity of the building energy consumption analysis model based on genetic algorithm improved BP neural network algorithm (GABP) in this paper is relatively high, which is more accurate than the results of the traditional BP neural network model, and the relative error of the analysis model is reduced from 11.56% to 8.13%, which proves that the GABP can be better suitable for the study of school building energy consumption analysis model, It is applied to the prediction of building energy consumption, which lays a foundation for the realization of carbon neutralization in the South expansion plan of Yangtze University. [ABSTRACT FROM AUTHOR]

In recent decades, international shipping trade has been developing continuously. The ship is the main transportation carrier of international shipping, and the power equipment on the ship is in an absolutely critical position, and its working condition is also directly related to the safe running of the ship. Therefore, the research on the ship's power plant and fault diagnosis system is particularly important. Due to the actual operation of the ship power plant, the characteristics of the components are inevitably changed. Therefore, the corresponding equipment fault diagnosis technology also has a certain importance for the health management system of power equipment. As for the problem that the current ship equipment fault identification method is not widely applicable and the accuracy is not high enough, this paper aims to make the ship fault diagnosis faster and more accurately. It effectively solves the problem of timeliness and accuracy of fault diagnosis. This paper takes ship power equipment as the research object, firstly, introduces and proposes a diagnosis method of adaptive neural network structure, and applies it to fault detection and estimation. Next, this paper uses the adaptive neural network model for ship fault diagnosis. The accuracy of the adaptive neural network designed in this paper is better than that of the conventional neural network, and when the number of training samples is small. It can still obtain an ideal network through training to ensure that the fault detection of power equipment health parameters has high accuracy. The simulation results show that, compared with the common methods, the network model can effectively reduce the fault diagnosis error. The correct rate of fault diagnosis is over 93%, which improves the speed, accuracy, and applicability of fault diagnosis. [ABSTRACT FROM AUTHOR]

Traditionally, the ampacity of an overhead transmission line (OHTL) is a static value obtained based on adverse weather conditions, which constrains the transmission capacity. With the continuous growth of power system load, it is increasingly necessary to dynamically adjust the ampacity based on weather conditions. To this end, this paper models the heat balance relationship of the OHTL based on a BP neural network using Bayesian optimization (BO-BP). On this basis, an OHTL ampacity prediction method considering the model error is proposed. First, a two-stage current-stepping ampacity prediction model is established to obtain the initial ampacity prediction results. Then, the risk control strategy of ampacity prediction considering the model error is proposed to correct the ampacity based on the quartile of the model error to reduce the risk of the conductor overheating caused by the model error. Finally, a simulation is carried out based on the operation data of a 220-kV transmission line. The simulation results show that the accuracy of the BO-BP model is improved by more than 20% compared with the traditional heat balance equation. The proposed ampacity prediction method can improve the transmission capacity by more than 150% compared with the original static ampacity. [ABSTRACT FROM AUTHOR]

With the rapid development of China's construction industry and the acceleration of urbanization, large‐scale public building projects are becoming increasingly important in urban development, and the risk management problems of them should be pay more attention to. Based on the integration of back propagation (BP) neural network and building information model (BIM) technology, this paper carries out the research on risk management process of the whole life cycle of large public buildings and identifies the risk factors of large public buildings from the application dimension and the management dimension. The risk management evaluation index system is constructed and identified, and assessment, early warning, prevention, and control of risk management are applied and analyzed throughout the process. The international large public sports center project is used as a case study to establish a BIM model, while the BP neural network risk management model is used for prediction and calculation. The results of this study show that, first, the maximum deviation rate of the output indicators of the BP neural network risk model is 3.57% in the design period (B2) and the minimum deviation rate is 0.00% in the commissioning period (B4), which verifies the reliability of the training results of the model. Second, the best effect of risk management in the whole life cycle of the building is in the investment period (B1) and the highest risk is in the construction period (B3). Last, this paper constructs a new risk management framework to realise the risk management of the whole cycle of construction projects from design to operation, which helps to improve the management level and risk response ability of construction projects and ensure the smooth and sustainable development of the whole life cycle of construction. [ABSTRACT FROM AUTHOR]

Neural network models, such as BP, LSTM, etc., support only numerical inputs, so data preprocessing needs to be carried out on the categorical variables to convert them into numerical data. For unordered multi-categorical variables, existing encoding methods may produce dimensional catastrophes and may also introduce additional order misrepresentation and distance bias in neural network computation. To solve the above problems, this paper proposes an unordered multi-categorical variable encoding method O-AE using orthogonal matrix for encoding and encoding representation learning and dimensionality reduction via an autoencoder. Bayesian optimization is used for hyperparameter optimization of the autoencoder. Finally, seven experiments were designed with the basic O-AE, Bayesian optimization of the hyperparameters of the autoencoder for O-AE, and other encoding methods to encode unordered multi-categorical variables in five datasets, and they were input into a BP neural network to carry out target prediction experiments. The results show that the experiments using O-AE and O-AE-b have better prediction results, proving that the method proposed in this paper is highly feasible and applicable and can be an optional method for the data processing of unordered multi-categorical variables. [ABSTRACT FROM AUTHOR]

This paper presents an optimization method for the design of marine metal sandwich plates to enhance the anti-blast performance of ship structures. The proposed method combines a neural network with the sparrow search algorithm to efficiently optimize the structure. The orthogonal design method is utilized to select appropriate sample points for the surrogate model. Dynamic responses of the ship under blast loading are obtained using the ABAQUS finite element software. By establishing an anti-blast surrogate model, the optimization problem is simplified. To overcome the limitations of the traditional backpropagation neural network, genetic algorithm and Adam algorithm are employed. Finally, the optimal solution is obtained based on the sparrow search algorithm. The results enable us to determine the structural dimensions with the best blast resistance. The proposed optimisation method can provide inspiration for improving the safety of ships. [ABSTRACT FROM AUTHOR]

With the rapid development of urban rail transportation, people's demand for subways has gradually manifested itself. The inherent complex attributes of subway project construction determine that subway project construction has a relatively high risk, resulting in huge losses. This paper takes the opportunistic behavior of the subway project as the research object, proposes the opportunistic behavior identification process, and constructs the opportunistic behavior identification model based on the BP neural network. Firstly, through the collection and analysis of subway accident cases, the main forms of opportunistic behavior are summarized, and the primary characteristic indicators for opportunistic behavior recognition are extracted using cluster analysis. Secondly, a recognition model based on a BP neural network is designed. The number of neurons in the input layer, hidden layer, and output layer of the model is determined, and the recognition model is subsequently trained and tested to validate its feasibility. Finally, the constructed opportunistic behavior recognition model is applied to an actual subway construction project, revealing that the construction enterprise of the project in question exhibits a high level of opportunistic behavior risk. Overall, the research results of this paper have important theoretical significance and practical value for the management level of subway project construction enterprises under the new situation and the identification and governance of opportunistic behavior of subway project construction enterprises. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent control method applying on numerical virtual flight is proposed. The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a good application prospect. Firstly, a numerical virtual flight simulation model based on overlapping dynamic mesh technology is constructed. In order to verify the accuracy of the dynamic grid technology and the calculation of unsteady flow, a numerical simulation of the basic finner projectile without control is carried out. The simulation results are in good agreement with the experiment data which shows that the algorithm used in this paper can also be used in the design and evaluation of the intelligent controller in the numerical virtual flight simulation. Secondly, combined with the real-time control requirements of aerodynamic, attitude and displacement parameters of the projectile during the flight process, the numerical simulations of the basic finner projectile’s pitch channel are carried out under the traditional PID(Proportional-Integral-Derivative) control strategy and the intelligent PID control strategy respectively. The intelligent PID controller based on BP(Back Propagation) neural network can realize online learning and self-optimization of control parameters according to the acquired real-time flight parameters. Compared with the traditional PID controller, the concerned control variable overshoot, rise time, transition time and steady state error and other performance indicators have been greatly improved, and the higher the learning efficiency or the inertia coefficient, the faster the system, the larger the overshoot, and the smaller the stability error. The intelligent control method applying on numerical virtual flight is capable of solving the complicated unsteady motion and flow with the intelligent PID control strategy and has a strong promotion to engineering application. [ABSTRACT FROM AUTHOR]

According to related research, different body temperatures, heart rates, and locomotor behaviors of small-tailed cold goats can represent the physical condition of the goats themselves and are used as direct evidence for evaluating the physical health status of small-tailed cold goats. In this paper, we designed and tested a system for predicting the health status of small-tailed cold sheep based on wearable information monitoring technology. To test the system, sheep wearable devices were worn on 36 small-tailed cold sheep of different ages and inconsistent health conditions at different time points from May to October. A SLBAS-BP neural network model for predicting the health condition of small-tailed cold sheep was established using the collected and processed data, which overcame the problem that the traditional gradient descent method in the BP neural network is prone to fall into local optimization leading to insufficient prediction ability. The correct prediction rates of the improved BP neural network for the four health conditions of healthy, sub-healthy, fever, and disease were 98.4%, 94.5%, 90.4%, and 98.7%, respectively, and the average correct prediction rate of the four conditions was 5.8% higher than that before the improvement, reaching 95.2%. [ABSTRACT FROM AUTHOR]

This paper presents models for UAV aerial-towed cables in free-end and fixed-end configurations, crucial for tasks like communication and aerial charging. By establishing a quasi steady-state model, computational results on cable shapes are obtained. To accelerate computations, a backpropagation (BP) neural network prediction model is trained, significantly reducing the computation time. An evaluation function has been developed that integrates both aircraft performance and cable shape considerations to evaluate circling parameters across various states. This function integrates techniques such as BP neural networks and particle swarm optimization (PSO) to refine parameters such as velocities and bank angles for both free-end and fixed-end cables. The results show that the BP neural network accurately predicts cable shapes, achieving a maximum error of 5% in towing force and verticality. Additionally, PSO efficiently optimizes circling parameters, thereby enhancing the effectiveness of the evaluation function in identifying optimal solutions. This approach significantly improves the efficiency of determining optimal circling parameters for UAV aerial-towed cables, thereby contributing to their operational efficacy. [ABSTRACT FROM AUTHOR]

Environmental issues have driven the booming development of lithium-ion battery technology, and improving the accuracy of state-of-charge (SOC) measurements will play an important role in prolonging battery cycle life and improving safety. In this paper, an improved genetic-back propagation network construction strategy is built to stably and accurately estimate the SOC at different temperatures. The design of the established SOC neural network estimation model is optimized by using a genetic algorithm to initialize the weights and thresholds of the backpropagation (BP) neural network. Then, the above algorithms are validated using Dynamic Stress Test (DST) and Beijing Bus Dynamic Stress Test (BBDST) datasets. The experimental results show that the MAE and RMSE of the SOC estimation results based on the BBDST dataset are 0.0133, 0.0143 and 0.0058, 0.0082 under the conditions of − 5 °C and 35 °C. Therefore, the improved genetic-back propagation network construction algorithm has a more stable iterative process and higher estimation accuracy, which provides a new method for SOC performance estimation of lithium-ion batteries. [ABSTRACT FROM AUTHOR]

Featured Application: This study is aimed at the detection of sulfur dioxide concentration in the real environment. Due to the vast territory of China and the large temperature difference span between the north and the south, the current sulfur dioxide detection devices find it difficult to meet the requirements of portability, low-cost delivery, and temperature adaptability. A reliable real-time monitoring device and detection algorithm for sulfur dioxide concentration using PMT are proposed. Air is the environmental foundation for human life and production, and its composition changes are closely related to human activities. Sulfur dioxide (SO2) is one of the main atmospheric pollutants, mainly derived from the combustion of fossil fuels. But SO2 is a trace gas in the atmosphere, and its concentration may be less than one part per billion (ppb). This paper is based on the principle of photoluminescence and uses a photomultiplier tube (PMT) as a photoelectric converter to develop a device for real-time detection of SO2 concentration in the atmosphere. This paper focuses on the impact of noise interference on weak electrical signals and uses wavelet transform to denoise the signals. At the same time, considering that the photoelectric system is susceptible to temperature changes, a multi parameter fitting model is constructed, and a BP neural network is used to further process the signal, separating the real data from the original data. Finally, a high-precision and wide-range trace level sulfur dioxide concentration detection device and algorithm were obtained. [ABSTRACT FROM AUTHOR]

With the increase in elevator usage, more and more elevator real-time monitoring equipment is being applied to the operation of elevators. Traditional elevator monitoring equipment adopts a multi-sensor decentralized installation and layout, and the monitoring accuracy is low, which directly affects the effective alarm of the monitoring system; however, existing online monitoring systems cannot quickly alarm for faults. Aiming to solve the above problems, an elevator online monitoring system based on narrow-band Internet of Things (NB-IoT) is designed. The system is highly integrated with an STM32 main control chip, a six-axis acceleration gyroscope sensor, and an air pressure sensor to realize the edge calculation of the monitoring system. At the same time, this paper eliminates the temperature drift of the pressure sensor by using a temperature compensation algorithm and inputs the extracted characteristic parameters into the BP neural network for training to eliminate the zero drift so as to obtain the real-time height data of the elevator. The six-axis acceleration gyroscope sensor is used to calculate the posture so as to avoid the problem that a three-axis acceleration sensor or a three-axis gyroscope sensor alone cannot obtain accurate posture data. In order to further improve the monitoring accuracy, the peak-to-peak value of the signal is calculated by using a 95% confidence interval algorithm to reduce the suppression of the high-frequency components of the signal by noise and ensure that the signal has a large signal-to-noise ratio so that the obtained elevator car posture and vibration operation data are more accurate. Finally, the effectiveness of the proposed method is verified by experiments. [ABSTRACT FROM AUTHOR]

Ethnic minority resources are very rich and contain rich historical resources and culture. Under the impact of modern information technology, the development of minority resources and the inheritance of ethnic culture are facing many challenges, and the current school education is lagging behind in exploring the ecological resources of minority groups, which makes the integration of creative education and minority groups encounter a bottleneck. In response to this situation, we make full use of the platform of creative education to actively explore the traditional skills contained in the lives of ethnic minorities. In the evaluation of creative education, we should not only focus on the evaluation of students' works, but also on the improvement of students' knowledge of various creative tools, their ability to use comprehensive subject knowledge, hands-on ability, solution ability and creativity ability during the whole learning process. Based on this, this paper proposes a back propagation neural network (BPNN)-based quality evaluation method for creative education to evaluate the quality of creative education from multiple dimensions. Experiments and comparisons show that the BPNN-based evaluation method proposed in this paper can better evaluate the whole process of creative education and help the further development of creative education in minority regions. [ABSTRACT FROM AUTHOR]

Large deployable space mechanisms are widely used in the field of aerospace and have been paid increasingly high attention recently. The satellite antenna expansion system is the classic large deployable space mechanism. However, during the expansion of the satellite antenna deployable mechanism, the expansion accuracy is affected by the existing various uncertain factors which even result in scraping the satellite. For example, the hinge locking error has the significant influence on the deployment accuracy of the satellite antenna panel. In term of this issue, considering the advantage of the back‐propagation (BP) neural network for the high dimensional nonlinear problem, this paper mainly adopts it to analyze the impact of hinge locking error on the expansion accuracy of the antenna panel. The results show that the probability of the actual flatness deviation of the satellite antenna panel falling in the required accuracy area is 99.56%, and the probability of the actual pointing angle deviation falling in the required accuracy area is 99.84%. [ABSTRACT FROM AUTHOR]

Time-of-use (TOU) is an effective price-based demand response strategy. A reasonable design of TOU strategy can effectively reduce the peak-valley difference, and then produce a lot of benefits (such as delaying power grid investment, reducing interruption cost, and improving reliability). However, changing peak-valley period has a great influence on the peak-valley difference and power supply reliability of power system. Therefore, this paper aims to investigate the optimal modification of peak-valley period considering reliability loss under multiple TOU schemes. Firstly, this paper presents a clustering model and algorithm of optimal load curve based on a minimum error iteration method. Secondly, an optimal modification of peak-valley period based on a dynamic load point method is proposed, and the traditional peak-valley difference is replaced by the global peak-valley difference to calculate the objective function. Thirdly, this paper establishes a load–reliability relation fitting model based on the back propagation neural network. Finally, the effectiveness and correctness of the proposed method are investigated by the Roy Billinton test system and reliability test system. [ABSTRACT FROM AUTHOR]

The diversity of attribute categories brings certain difficulties to data feature detection. In order to improve the accuracy and efficiency of feature detection, a hybrid attribute feature detection method for power system intelligent operation and maintenance big data based on improved random forest algorithm is proposed. Clustering processing power system intelligent operation and maintenance big data, based on data clustering results to reduce the characteristics of data mixed attributes, reduce the scale of data processing, and discretize the data mixed attributes; BP neural network is used to preprocess the results. Make corrections to improve the accuracy of feature detection, use the improved random forest algorithm to classify the data, and improve the convergence speed of the method. Finally, the support vector machine method is used to realize the feature detection of data mixed attributes. The experimental results show that the feature detection accuracy and efficiency of the method designed in this paper are high, and more features can be detected, which verifies its effectiveness. The method designed in this paper has the minimum RMSE value and the maximum value is only 0.12, which is far lower than the RMSE value of the improved spectral clustering algorithm and multi-domain feature extraction method, and has high detection accuracy. [ABSTRACT FROM AUTHOR]

The foul odor of foul gas has many harmful effects on the environment and human health. In order to accurately assess this impact, it is necessary to identify specific malodorous components and levels. In order to meet the qualitative and quantitative identification of the components of malodorous gas, an electronic nose system is developed in this paper. Both principal component analysis (PCA) and linear discriminant analysis (LDA) were used to reduce the dimensionality of the collected data. The reduced-dimensional data are combined with a support vector machine (SVM) and backpropagation (BP) neural network for classification and recognition to compare the recognition results. Regarding qualitative recognition, this paper selects the method of LDA combined with the BP neural network after comparison. Experiments show that the qualitative recognition rate of this method in this study can reach 100%, and the amount of data after LDA dimensionality reduction is small, which speeds up the pattern speed of recognition. Regarding quantitative identification, this paper proposes a prediction experiment through Partial least squares (PLS) and BP neural networks. The experiment shows that the average relative error of the trained BP network is within 6%. Finally, the experiment of quantitative analysis of malodorous compound gas by this system shows that the maximum relative error of this method is only 4.238%. This system has higher accuracy and faster recognition speed than traditional methods. [ABSTRACT FROM AUTHOR]

Ultrasonic cutting technology is widely used in the processing of Nomex honeycomb composites, but the identification of wear state and life prediction of ultrasonic straight-edge cutter are one of the problems to be solved urgently. In this paper, based on the tool wear mechanism, the multi-dimensional parameters of cutting force and cutting sound signal are extracted through experiments, the tool wear characteristics are established, and the tool wear stages are divided based on BP neural network and K-nearest neighbor cluster analysis, we established a tool wear recognition model with an accuracy rate of 92.3%. Further, a remaining life prediction model is established based on the HMM model, which can accurately predict the service life of the tool. The research on the wear state and remaining life of ultrasonic tools in this paper provides theoretical significance for the diagnosis of acoustic system faults and has important engineering value for reducing enterprise economic expenses and improving parts processing quality. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles (AUVs) are now widely used in both civilian and military applications; however, wireless charging underwater often faces difficulties such as disturbances from ocean currents and errors in device positioning, making proper alignment of the charging devices challenging. Misalignment between the primary and secondary coils can significantly impact the efficiency and power of the wireless charging system energy transfer. To address the issue of misalignment in wireless charging systems, this paper proposes a multiple transfer coil wireless power transfer (MTCWPT) system based on backpropagation (BP) neural network control combined with nonsingular terminal sliding mode control (NTSMC) to enhance further the system robustness and efficiency. To achieve WPT in the ocean, a coil shielding case structure was equipped. In displacement experiments, the proposed multi-transmitting coil system could achieve stable power transfer of 40 W and efficiency of over 78.5% within a displacement range of 8 cm. The system robustness was also validated. This paper presents a new AUV energy supply solution based on MTCWPT. The proposed MTCWPT system can significantly improve the navigation performance of AUVs. [ABSTRACT FROM AUTHOR]

Mega-constellation network traffic forecasting provides key information for routing and resource allocation, which is of great significance to the performance of satellite networks. However, due to the self-similarity and long-range dependence (LRD) of mega-constellation network traffic, traditional linear/non-linear forecasting models cannot achieve sufficient forecasting accuracy. In order to resolve this problem, a mega-constellation network traffic forecasting model based on EMD (empirical mode decomposition)-ARIMA (autoregressive integrated moving average) and IGWO (improved grey wolf optimizer) optimized BPNN (back-propagation neural network) is proposed in this paper, which makes comprehensive utilization of linear model ARIMA, non-linear model BPNN and optimization algorithm IGWO. With the enhancement of the global optimization capability of a BPNN, the proposed hybrid model can fully realize the potential of mining linear and non-linear laws of mega-constellation network traffic, hence improving the forecasting accuracy. This paper utilizes an ON/OFF model to generate historical self-similar traffic to forecast. RMSE (root mean square error), MAE (mean absolute error), R-square and MAPE (mean absolute percentage error) are adopted as evaluation indexes for the forecasting effect. Comprehensive experimental results show that the proposed method outperforms traditional constellation network traffic forecasting schemes, with several improvements in forecasting accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Big data analysis technologies and machine learning techniques are essential for examining and forecasting the state of network security as global concerns about cyber security grow. Models for monitoring network security have a number of challenges, including resource consumption, inaccuracies, low processing efficiency, and incompatibility with real-time and large-scale scenarios. This paper proposes a novel approach to Network Security Situation Awareness (NS-SA) using Big Data (BD) analytics and machine learning. The proposed approach addresses the limitations of existing NS-SA models by leveraging data purification and simplification techniques, and by employing an updated back propagation (BP) neural network to construct an NS BD analysis model. The paper provides a comprehensive explanation of the model's structure and outlines the relevant model techniques. Extensive testing has been conducted to ensure the model's accuracy and applicability in understanding NS scenarios. This study focuses on MATLAB and Python-based implementation of a neural network for network security using a big data approach. The results demonstrate the potential and value of the proposed model in accurately assessing and forecasting NS conditions. The proposed approach has several advantages over existing NS-SA models. It is more efficient in terms of resource usage, it is more accurate in its analysis of network data, It is more applicable in real-time and large-scale scenarios and it is more robust to noise and heterogeneity in network data. [ABSTRACT FROM AUTHOR]

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In order to solve the problem of the low end positioning accuracy of large hydraulic rock drilling robotic arms due to machining error and the working environment, this paper proposes an end positioning error compensation method based on an Improved Secretary Bird Optimization Algorithm (ISBOA) optimized Back Propagation (BP) neural network. Firstly, the good point set strategy is used to initialize the secretary bird population position to make the initial population distribution more uniform and accelerate the convergence speed of the algorithm. Then, the ISBOA is used to optimize the initial weights and biases of the BP neural network, which effectively overcomes the defect of the BP neural network falling into a local optimum. Finally, by establishing the mapping relationship between the joint value of the robot arm and the end positioning error, the error compensation is realized to improve the positioning accuracy of the rock drilling robot arm. The experimental results show that the average positioning error of the rock drilling robotic arm is reduced from 187.972 mm to 28.317 mm, and the positioning accuracy is improved by 84.94%, which meets the engineering requirements. [ABSTRACT FROM AUTHOR]

The current seismic prediction methods of the shale brittleness index are all based on the pre-stack seismic inversion of elastic parameters, and the elastic parameters are transformed by Rickman and other simple linear mathematical relationship formulas. In order to address the low accuracy of the seismic prediction results for the brittleness index, this study proposes a method for predicting the brittleness index of shale reservoirs based on an error backpropagation neural network (BP neural network). The continuous static rock elastic parameters were calculated by fitting the triaxial test data with well logging data, and the static elastic parameters with good correlation with the brittleness index of shale minerals were selected as the sample data of the BP neural network model. A dataset of 1970 data points, characterized by Young's modulus, Poisson's ratio, shear modulus, and the mineral brittleness index, was constructed. A total of 367 sets of data points from well Z4 were randomly retained as model validation data, and 1603 sets of data points from the other three wells were divided into model training data and test data at a ratio of 7:3. The calculation accuracy of the model with different numbers of nodes was analyzed and the key parameters of the BP neural network structure such as the number of input layers, the number of output layers, the number of hidden layers, and the number of neurons were determined. The gradient descent method was used to determine the weight and bias of the model parameters with the smallest error, the BP neural network model was trained, and the stability of the brittleness index prediction model of the BP neural network was verified by posterior data. After obtaining Young's modulus, Poisson's ratio, and shear modulus through pre-stack seismic inversion, the BP neural network model established in this study was used to predict the brittleness index distribution of the target layer in the study area. Compared with the conventional Rickman method, the prediction coincidence rate is 69.16%, and the prediction coincidence rate between the prediction results and the real value is 95.79%, which is 26.63% higher. The BP neural network method proposed in this paper provides a reliable new method for seismic prediction of the shale reservoir brittleness index, which has important practical significance for clarifying the shale gas development scheme and improving shale gas exploitation efficiency. [ABSTRACT FROM AUTHOR]

Launcher efficiency is an important index for evaluating the performance of the electromagnetic launcher, and it reflects the ability of the launcher to convert input electrical energy into kinetic energy of the armature. In this paper, the launcher efficiency is taken as the objective function of bore parameter optimization, and particle swarm optimization is used to optimize the initial parameters of the BP neural network to improve the accuracy of the neural network in predicting launcher efficiency. The results show the following: (1) The predicted efficiency of the launcher shows the same trend as the experimental results. When the ratio of rail separation and rail height is greater than 1.75, the rate of change in the launcher efficiency curve decreases as the rail separation increases. (2) The weight of the influence of each parameter on the launcher efficiency follows the following law: convex arc height > rail separation > rail height > rail thickness. (3) The mean absolute error of the BP neural network prediction is 0.70%; after optimization by PSO, the mean absolute error is reduced to 0.28% and the mean relative accuracy is improved from 0.9774 to 0.9910, which indicates the feasibility of the PSO-BP neural network for the prediction of the launcher efficiency of an electromagnetic launcher. [ABSTRACT FROM AUTHOR]

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Concrete linings containing cold joint defects may crack or detach under the aerodynamic fatigue loading generated by high-speed train operation, which posing a serious threat to the normal operation of high-speed trains. However, there is currently no simulation method specifically for fatigue damage of concrete linings containing cold joints. Based on the Roe-Siegmund cycle cohesive force model, a cohesive force fatigue damage elements were developed. A large dataset was constructed through numerical simulation software to build a BP neural network for back-calculated parameter of cohesive force fatigue damage elements. By combining experimental data, fatigue damage parameters corresponding to different pouring interval cold joints were back-calculated. These back-calculated parameters were then incorporated into the numerical model to compare simulation results with experimental results to validate the applicability of cohesive force fatigue damage elements and back propagation neural networks (BP neural network). The research results show that the difference between the fatigue life and fracture process calculated by numerical simulation and experimental data is small, verifying the applicability of the method proposed in this paper. The pouring interval directly affects the initial strength of the cold joint interface and the starting conditions of fatigue damage. The possibility of fatigue damage and fracture of concrete components containing cold joints increases with the increase of pouring interval, while the variability of fatigue life decreases with the increase of pouring interval. Interface strength and thickness are the main factors affecting the possibility of fatigue damage occurrence and the variability of fatigue life. The research results can be used to analyze the damage and cracking status of concrete linings containing cold joints under aerodynamic fatigue loading. [ABSTRACT FROM AUTHOR]

Magnetic suspension balance systems (MSBSs) need to allow vehicle models to levitate stably in different attitudes, so it is difficult to ensure the stable performance of the system under different levitation gaps using a controller designed with single balance point linearization. In this paper, a levitation controller based on linear model predictive control and a back-propagation neural network (LMPC-BP) is proposed and simulated for single-point magnetic levitation. The deviation of the BP network is observed and compensated by an expansion state observer (ESO). The iterative BP neural network model is further updated using current data and feedback data from the ESO, and then the performance of the LMPC-BP controller is evaluated before and after the update. The simulation results show that the LMPC-BP controller can achieve stable levitation at different gaps of the single-point magnetic levitation system. With further updating and iteration of the BP network, the controller anti-jamming performance is improved. [ABSTRACT FROM AUTHOR]

Aiming to address the timing skew mismatch in the time‐interleaved analog‐to‐digital converter (TIADC) system, this paper presents a timing skew mismatch calibration method based on a back propagation (BP) neural network optimized by an adaptive genetic algorithm (AGA). In this paper, a trained BP neural network is used to detect the timing skew mismatch in the TIADC system, and the variable delay line is used to calibrate it. In this paper, AGA is used to optimize the BP neural network, accelerating its training speed and improving the detection accuracy of timing skew mismatch in the system. The proposed approach boasts superior detection speed and accuracy compared to other methods. In this paper, an 18‐bit 1GS/S 4‐channel TIADC system is simulated and the timing skew mismatch in the system is corrected. Simulation results show that the proposed calibration method has fast detection speed, high detection accuracy, and calibration accuracy. After completing the timing skew mismatch correction, the performance of the TIADC system is dramatically improved. The effective number of bits (ENOB) of the system increases by 9.5 bits, and the spurious‐free dynamic range (SFDR) increases by 59.9 dB. [ABSTRACT FROM AUTHOR]

Deep-sea mining, as a critical direction for the future development of mineral resources, places significant importance on the mechanical characteristics of its transportation pipelines for the safety and efficiency of the entire mining system. This paper establishes a simulation model of the deep-sea mining system based on oceanic environmental loads and the mechanical theory of deep-sea mining transportation pipelines. Through a static analysis, the effective tension along the pipeline length, the maximum values of bending moment, and the minimum values of bending radius are determined as critical points for the dynamic analysis of pipeline mechanical characteristic monitoring. A dynamic simulation analysis of the pipeline's mechanical characteristics was conducted, and simulation sensor data were obtained as inputs for the prediction model construction. A prediction model of pipeline mechanical characteristics based on the BP neural network was constructed, with the model's prediction correlation coefficients all exceeding 0.95, enabling an accurate prediction of pipeline state parameters. [ABSTRACT FROM AUTHOR]

This paper comprehensively considers 12 indicators, including temperature, component content, solid–liquid ratio, free volume ratio, atomic cluster as characteristic parameters, to establish a back-propagation (BP) neural network prediction model for the viscosity of multi-element titanium-containing iron-based melts. The comprehensive model is dissected into distinct sub-models based on specific characteristic parameters, including the temperature and composition (T&C)-BP, Liquid structure parameters (LS)-BP, and Solid-phase particle parameters (S)-BP sub-models. The performance and applicability of each sub-model are rigorously analyzed, providing valuable insights into their respective scopes and limitations. By comparing the actual molten iron viscosity with the model predicted value, it was found that, the relative errors for all predicted values were found to be within 10%. The relative error for individual samples at 1350 °C was an impressive 1.3%. Furthermore, a substantial 56% of the predictions exhibited a relative error of less than 5%. [ABSTRACT FROM AUTHOR]

In the rapid development of high and new technology, the intelligence and integration of modern equipment are constantly improving. Power electronics technology is one of the indispensable key technologies in any high and new technology. In this paper, a power electronics circuit fault diagnosis based on improved particle swarm optimization neural network is proposed, the algorithm design of particle swarm optimization algorithm neural network is introduced, and the improved PS0 algorithm, standard PS0 algorithm, and BP algorithm optimized neural network are applied to the fault diagnosis classification system of rectifier circuits. The results show that the parameters of the basic (particle swarm optimization) algorithm are as follows: the parameter value of the basic PSO algorithm is the number of particles is 30, W decreases from 0.9 to 0.4 linearly with the increase of iterations, and the number of iterations is 300. The BP algorithm uses the traingdx training function. The transfer functions of the hidden layer and the output layer are hyperbolic tangent sigmoid and Purelin function, respectively. The target error e = 0.01. The superiority and effectiveness of the neural network diagnosis model of the improved PS0 algorithm are shown in this paper. This method can solve the fault diagnosis problem of the double-bridge parallel rectifier circuit. [ABSTRACT FROM AUTHOR]