A Temperature Prediction Model for Flexible Electronic Devices Based on GA-BP Neural Network and Experimental Verification.

The problem that the thermal safety of flexible electronic devices is difficult to evaluate in real time is addressed in this study by establishing a BP neural network (GA-BPNN) temperature prediction model based on genetic algorithm optimisation. The model uses a BP neural network to fit the functional relationship between the input condition and the steady-state temperature of the equipment and uses a genetic algorithm to optimise the parameter initialisation problem of the BP neural network. To overcome the challenge of the high cost of obtaining experimental data, finite element analysis software is used to simulate the temperature results of the equipment under different working conditions. The prediction variance of the GA-BPNN model does not exceed 0.57 °C and has good robustness, as the model is trained according to the simulation data. The study conducted thermal validation experiments on the temperature prediction model for this flexible electronic device. The device reached steady state after 1200 s of operation at rated power. The error between the predicted and experimental results was less than 0.9 °C, verifying the validity of the model's predictions. Compared with traditional thermal simulation and experimental methods, this model can quickly predict the temperature with a certain accuracy and has outstanding advantages in computational efficiency and integrated application of hardware and software.

Carbon emission prediction of construction industry in Sichuan Province based on the GA-BP model.

The reduction of the carbon emissions of construction industry is urgent. Therefore, it is essential to accurately predict the carbon emissions of the provincial construction industry, which can support differentiation emission reduction policies in China. This paper proposes a carbon emission prediction model that optimizes the backpropagation (BP) neural network by genetic algorithm (GA) to predict carbon emission of construction industry, or "GA-BP". To begin with, the carbon emissions of construction industry in Sichuan Province from 2000 to 2020 are calculated by the emission factor method. Further, the electricity correction factor is introduced to eliminate the regional difference in electricity carbon emission coefficient. Finally, four factors are selected by the grey correlation analysis method to predict the carbon emission of construction industry in Sichuan Province from 2021 to 2025. The results show that the carbon emissions of construction industry in Sichuan Province have been trending up in the past two decades, with an average increase rate of 10.51%. The GA-BP model is a high-precision prediction model to predict carbon emissions of construction industry. The mean absolute percentage error (MAPE) of the model is only 6.303%, and its coefficient of determination is 0.853. Moreover, the carbon emissions of construction industry in Sichuan Province will reach 8891.97 million tons of CO2 in 2025. The GA-BP model can effectively predict the future carbon emissions of construction industry in Sichuan Province, which provides a new idea for the green and sustainable development of construction industry in Sichuan Province.

CO2 emission prediction based on carbon verification data of 17 thermal power enterprises in Gansu Province.

The energy and power industry is an important field for CO2 emission reduction. The CO2 emitted by thermal power enterprises is a major cause of global climate change, and also a key challenge for China to achieve the goals of "carbon peaking and carbon neutrality." Therefore, it is essential to scientifically and accurately predict the CO2 emissions of key thermal power enterprises in the region. This will guide carbon reduction strategies and policy recommendations for leaders, and also provide a valuable reference for similar regions globally. This study utilizes the factor analysis method to extract the common factors influencing CO2 emissions based on the carbon verification data of 17 thermal power enterprises in Gansu Province. Additionally, the DISO (distance between indices of simulation and observation) index is employed to comprehensively evaluate three prediction models, namely multiple linear regression, support vector regression, and GA-BP neural network. Ultimately, this study provides a reasonable prediction of CO2 emissions for the aforementioned enterprises in Gansu Province. The results show that the three common factors obtained by factor analysis, namely energy consumption and output factor, energy quality factor, and energy efficiency factor, can effectively predict the CO2 emissions from thermal power enterprises. In the three prediction models, GA-BP neural network has the best overall performance with DISO value of 0.95, RMSE value of 11848.236, and MAE value of 7880.543. Over the period 2022-2030, CO2 emissions from 17 thermal power enterprises in Gansu Province are predicted to increase. Under the low-carbon, scenario baseline, and high-carbon scenarios, the CO2 emissions will reach 71.58 Mt, 79.25 Mt, and 87.97 Mt, respectively, by 2030.

Research on the Prediction Model of Engine Output Torque and Real-Time Estimation of the Road Rolling Resistance Coefficient in Tracked Vehicles.

Road parameter identification is of great significance for the active safety control of tracked vehicles and the improvement of vehicle driving safety. In this study, a method for establishing a prediction model of the engine output torques in tracked vehicles based on vehicle driving data was proposed, and the road rolling resistance coefficient f was further estimated using the model. First, the driving data from the tracked vehicle were collected and then screened by setting the driving conditions of the tracked vehicle. Then, the mapping relationship between the engine torque Te, the engine speed ne, and the accelerator pedal position ß was obtained by a genetic algorithm-backpropagation (GA-BP) neural network algorithm, and an engine output torque prediction model was established. Finally, based on the vehicle longitudinal dynamics model, the recursive least squares (RLS) algorithm was used to estimate the f. The experimental results showed that when the driving state of the tracked vehicle satisfied the set driving conditions, the engine output torque prediction model could predict the engine output torque T^e in real time based on the changes in the ne and ß, and then the RLS algorithm was used to estimate the road rolling resistance coefficient f^. The average coefficient of determination R of the T^e was 0.91, and the estimation accuracy of the f^ was 98.421%. This method could adequately meet the requirements for engine output torque prediction and real-time estimation of the road rolling resistance coefficient during tracked vehicle driving.

Machine learning method for predicting cadmium concentrations in rice near an active copper smelter based on chemical mass balance.

Identification the sources of heavy metals can effectively control and prevent agricultural soil pollution. Here we performed a three-year mass balance study along a gradient of soil pollution near a smelter to quantify the potential contribution and net cadmium (Cd) fluxes and predict Cd concentration in rice grains by multiple regression (MR) and back propagation (BP) neural network. The Cd inputs were mainly from the irrigation water (54.6-60.8%) in the moderately polluted and background sites but from atmospheric deposition (90.9%) in the highly polluted site. The Cd outputs were mainly from the surface runoff (55.8-59.5%) in the moderately polluted and background sites, but from Sedum plumbizincicola phytoextraction (83.6%) in the highly polluted site. The soil Cd concentrations, the annual fluxes of atmospheric deposition, pesticides and fertilizers, irrigation water, surface runoff, and leaching water were selected as the dependent factors to predict Cd concentrations in rice grains. The genetic algorithms (GA)-BP neural network model gives the best prediction accuracy compared to the BP neural network model and multivariate regression analysis. The major implication is that the health risks through the consumption of rice can be rapidly assessed based on the Cd concentrations in rice grains predicted by the model.

Optimal Underwater Acoustic Warfare Strategy Based on a Three-Layer GA-BP Neural Network.

A defense platform is usually based on two methods to make underwater acoustic warfare strategy decisions. One is through Monte-Carlo method online simulation, which is slow. The other is by typical empirical (database) and typical back-propagation (BP) neural network algorithms based on genetic algorithm (GA) optimization, which is less accurate and less robust. Therefore, this paper proposes a method to build an optimal underwater acoustic warfare feedback system using a three-layer GA-BP neural network and dropout processing of the neural network to prevent overfitting, so that the three-layer GA-BP neural network has adequate memory capability while still having suitable generalization capability. This method improves the accuracy and stability of the defense platform in making underwater acoustic warfare strategy decisions, thus increasing the survival probability of the defense platform in the face of incoming torpedoes. This paper uses the optimal underwater acoustic warfare strategies corresponding to incoming torpedoes with different postures as the sample set. Additionally, it uses a three-layer GA-BP neural network with an overfitting treatment for training. The prediction results have less error than the typical single-layer GA-BP neural network, and the survival probability of the defense platform improves by 6.15%. This defense platform underwater acoustic warfare strategy prediction method addresses the impact on the survival probability of the defense platform due to the decision speed and accuracy.

Artificial neural network for cytocompatibility and antibacterial enhancement induced by femtosecond laser micro/nano structures.

The failure of orthopedic and dental implants is mainly caused by biomaterial-associated infections and poor osseointegration. Surface modification of biomedical materials plays a significant role in enhancing osseointegration and anti-bacterial infection. In this work, a non-linear relationship between the micro/nano surface structures and the femtosecond laser processing parameters was successfully established based on an artificial neural network. Then a controllable functional surface with silver nanoparticles (AgNPs) to was produced to improve the cytocompatibility and antibacterial properties of biomedical titanium alloy. The surface topography, wettability, and Ag+ release were carefully investigated. The effects of these characteristics on antibacterial activity and cytocompatibilty were also evaluated. Results show that the prepared surface is hydrophobic, which can prevent the burst release of Ag+ in the initial stage. The prepared surface also shows both good cytocompatibility toward the murine calvarial preosteoblasts MC3T3-E1 cells (derived from Mus musculus (mouse) calvaria) and good antibacterial effects against Gram-negative (E. coli) and Gram-positive (S. aureus) bacteria, which is caused by the combined effect of appropriate micro/nano-structured feature and reasonable Ag+ release rate. We do not only clarify the antibacterial mechanism but also demonstrate the possibility of balancing the antibacterial and osteointegration-promoting properties by micro/nano-structures. The reported method offers an effective strategy for the patterned surface modification of implants.

Investigation of the Temperature Compensation of Piezoelectric Weigh-In-Motion Sensors Using a Machine Learning Approach.

Piezoelectric ceramics have good electromechanical coupling characteristics and a high sensitivity to load. One typical engineering application of piezoelectric ceramic is its use as a signal source for Weigh-In-Motion (WIM) systems in road traffic monitoring. However, piezoelectric ceramics are also sensitive to temperature, which affects their measurement accuracy. In this study, a new piezoelectric ceramic WIM sensor was developed. The output signals of sensors under different loads and temperatures were obtained. The results were corrected using polynomial regression and a Genetic Algorithm Back Propagation (GA-BP) neural network algorithm, respectively. The results show that the GA-BP neural network algorithm had a better effect on sensor temperature compensation. Before and after GA-BP compensation, the maximum relative error decreased from about 30% to less than 4%. The sensitivity coefficient of the sensor reduced from 1.0192 × 10-2/°C to 1.896 × 10-4/°C. The results show that the GA-BP algorithm greatly reduced the influence of temperature on the piezoelectric ceramic sensor and improved its temperature stability and accuracy, which helped improve the efficiency of clean-energy harvesting and conversion.

Carbon emissions index decomposition and carbon emissions prediction in Xinjiang from the perspective of population-related factors, based on the combination of STIRPAT model and neural network.

In the present study, the STIRPAT model was adopted to examine the impacts of several factors on dioxide emissions using the time series data from 2000 to 2019 in Xinjiang. The said factors included population aging, urbanization, household size, per capita GDP, number of vehicles, per capita mutton consumption, education level, and household direct energy consumption structure. Findings were made that the positive effects of urbanization, per capita GDP, per capita mutton consumption and education on carbon emissions were obvious; the number of vehicles had the biggest positive impact on carbon dioxide emissions; and household size and household direct energy consumption structure had a significantly negative impact on carbon emissions. Based on the aforementioned findings, the GA-BP neural network was introduced to predict the carbon emission trend of Xinjiang in 2020-2050. The results reveal that the peak time of the low-carbon scenario was the earliest, between 2029 and 2033. The peak time of the middle scenario was later than low-carbon scenario, between 2032 and 2037, while the peak time of the high-carbon scenario was the latest and was unlikely to reach the peak before 2050.

Research on the Early-Warning Model of Network Public Opinion of Major Emergencies.

The rapid development of Internet in recent years has led to a proliferation of social media networks as people who can gather online to share information, knowledge, and opinions. However, the network public opinion tends to generate strongly misleading and a large number of messages can cause shocks to the public once major emergencies appear. Therefore, we need to make correct prediction regarding and timely identify a potential crisis in the early warning of network public opinion. In view of this, this study fully considers the features of development and the propagation characteristics, so as to construct a network public opinion early warning index system that includes 4 first-level indicators and 13 second-level indicators. The weight of each indicator is calculated by the "CRITIC" method, so that the comprehensive evaluation value of each time point can be obtained and the early warning level of internet public opinion can be divided. Then, the Back Propagation neural network based on Genetic Algorithm (GA-BP) is used to establish a network public opinion early warning model. Finally, the major public health emergency, COVID-19 pandemic, is taken as a case for empirical analysis. The results show that by comparing with the traditional classification methods, such as BP neural network, decision tree, random forest, support vector machine and naive Bayes, GA-BP neural network has a higher accuracy rate for early warning of network public opinion. Consequently, the index system and early warning model constructed in this study have good feasibility and can provide references for related research on internet public opinion.

Research on High Temperature Stamping Forming Performance and Process Parameters Optimization of 7075 Aluminum Alloy.

The stress strain curve of 7075 aluminum alloy in the temperature range of 310 °C to 410 °C was obtained by Gleeble-3800. By Nakazima test, the isothermal thermoforming limit diagrams of 7075 aluminum alloy at different deformation temperatures and stamping speeds were acquired. Moreover, the parameters of automotive S-rail hot stamping process were optimized by GA-BP neural network. The results show that the forming limit curve of 7075 aluminum alloy increases as the deformation temperature and stamping speed increase. The predicted optimal parameters for hot stamping of automotive S-rails by GA-BP neural network are: stamping speed is 50 mm/s, friction coefficient between die and blank is 0.1, and blank holder force is 5 kN. The maximum thinning rate at this process parameter is 9.37%, which provided a reference for 7075 aluminum alloy automotive S-rail hot stamping.

A GA-BP method of detecting carbamate pesticide mixture based on three-dimensional fluorescence spectroscopy.

In this paper, we have proposed a method to detect a mixture of carbamate pesticides using a back propagation network (BP), which is optimized by genetic algorithm (GA) for quantitative analysis. This method aims to combine the advantages of BP and GA to remedy their drawbacks. The training samples were taken as input, some performance indexes such as the predicted values, iteration time, mean squared error, correlation coefficient and recovery rate were compared between BP neural network and the constructed GA-BP model to evaluate the performance of two neural networks. Results show that the optimized GA-BP model can effectively predict the concentrations, the mean squared error and recovery rate are better. In addition, the correlation coefficient has a significant improvement. This study can provide a new way for detection of the pesticides mixture and help to analysis in a reliable way.

The classification model of type 2 diabetic peripheral neuropathy was studied based on GA-BP neural network model / 解放军医学杂志

Objective: To establish the genetic algorithm optimizing back-propagation (GA-BP) neural network model based on the clinical examination index for diagnosing type 2 diabetic peripheral neuropathy (DPN), and evaluate its diagnostic performance. Methods: A total of 2240 DPN patients and 2632 non-DPN patients were collected from the Hospital affiliated to Chongqing Medical University from January to December 2016, and univariate analysis was performed for 41 clinical test indicators of the two groups of patients with SPSS 21.0. Thirty-seven items of statistically significant variables were selected to establish the decision tree and Bayesian model with R software, MATLAB 2014a software was employed to establish the BP neural network and GA-BP neural network model, the advantages and disadvantages of these four models were compared with various evaluation parameters. Results: Using decision tree, the Bayesian, BP neural network and GA-BP neural network for 4872 cases of observation object model, the decision tree of The test sample accuracy with decision tree model was 93.4%, with Bayesian model was 70.0%, with BP neural network model was 98.9%, and with GA-BP neural network model was 99.5%. The areas under the ROC curve were 0.93, 0.72, 0.99 and 0.99, respectively. The Youden Indexes were 0.87, 0.59, 0.98 and 0.98, respectively. Conclusion: The GA-BP neural network established in present paper has a good computer-aided diagnosis function for type 2 diabetic peripheral neuropathy, but further clinical trials are still needed.

A novel method for predicting cadmium concentration in rice grain using genetic algorithm and back-propagation neural network based on soil properties.

Heavy metal pollution is a global ecological safety issue, especially in crops, where it directly threatens regional ecological security and human health. In this study, the back-propagation (BP) neural network optimized by the genetic algorithm (GA) was used to predict the concentration of cadmium (Cd) in rice grain based on influencing factors. As an intelligent information processing system, the GA-BP neural network could learn the laws of Cd movement in the soil-crop system through its own training and use the soil properties to predict the concentration of Cd in grain with high accuracy. The total soil Cd concentration, clay content, Ni concentration, cation exchange capacity (CEC), organic matter (OM), and pH have important impacts and interactions on Cd concentration in rice grain were selected as input factors of the prediction model based on Pearson's correlation analysis and GeoDetector. By using GA to optimize the initial weight, the prediction accuracy of the GA-BP neural network model was optimal compared with the BP neural network model and multiple regression analysis. Based on the Cd concentration predicted in grain by the model, human exposure and health risk can be assessed quickly, enabling measures to be taken in time to reduce the transfer of Cd from soil to the food chain.