Optimal Artificial Neural Network Research Articles

Fault causes and its detection in standalone PV system using ANN and GEO technique

Power generation systems using photovoltaic (PV) technology have become increasingly popular due to their high production efficiency. A partial shading defect is the most common defect in this system under the process of production, diminishing both the amount and quality of energy produced. This paper proposes an Artificial Neural Network and Golden Eagle Optimization based prediction of the fault and its detection in a standalone PV system to recover the optimum performance and diagnosis of the PV system. The proposed technique combines the Artificial Neural Network (ANN) and Golden Eagle Optimization (GEO) algorithm. The major contribution of this work is to raise PV systems' performance. The result is a defect in the classification and identification of an ANN is used. The use of GEO provides an efficient optimization technique for ANN training, which reduces the training time and improves the accuracy of the model. The proposed technique is executed on the MATLAB site and contrasted with different present techniques, like genetic algorithm (GA),Elephant Herding Optimization (EHO) and Particle Swarm Optimization (PSO). The findings displays that the proposed technique is more accurate and effective than the existing methodologies for detecting and diagnosing defects in PV systems.

Dynamic viscosity prediction for MWCNT-MgO (10%–90%)-SAE40 oil hybrid nano-lubricant using artificial neural network andmulti-dimensional nonlinear least square curve fitting

In this study, the dynamic viscosity of MWCNT-MgO (10%-90%)/SAE40 Oil Hybrid Nanofluid (HNF) is investigated. To this end, two methods are used including artificial neural network (ANN) and least square curve fitting. This nonlinear method uses a multi-dimensional least square strategy to determine coefficients of various parameters in the viscosity function. The viscosity of MWCNT-MgO (10%-90%)/SAE40 Oil HNF is predicted using three input parameters including solid volume fraction (SVF), temperature, and shear rate (SR). According to the obtained data, the optimum ANN to predict the viscosity of HNF as a function of inputs has 14 neurons with a nonlinear activation function resulting in mean square error or MSE=7.74e-4 and R2=0.999226 for all data. Moreover, temperature is the most influential parameter, and the rest of the parameters, have much fewer effects on the viscosity of MWCNT-MgO (10%-90%)/SAE40 Oil HNF. Furthermore, this can be seen in the obtained nonlinear fitted function through coefficients of input parameters. To be more precise, T coefficient is -91.2407 much higher than the solid volume fraction and shear rate with coefficients of 34.0366 and -0.166 respectively. The obtained ANN structure and nonlinear fitted function can be used for optimization purposes in future research. It is worth mentioning that although the fitted function by least square curve fitting is a very handy and symbolic equation, the ANN showed higher accuracy.

Optimizing energy expenditure in agricultural autonomous ground vehicles through a GPU-accelerated particle swarm optimization-artificial neural network framework

The accurate energy consumption prediction in Agricultural Ground Vehicles (AGVs) holds immense potential for optimizing operational efficiency and minimizing environmental impact. However, existing optimization methods for such prediction tasks often suffer from high computational demands, hindering their practical implementation. This paper introduces a ground-breaking approach that overcomes this limitation by leveraging the potent computational power of Graphics Processing Units (GPUs) to accelerate the optimization process dramatically. We propose a novel adaptation of the Particle Swarm Optimization (PSO) algorithm, specifically tailored to the intricate multi-objective challenges of AGV energy prediction. This framework harnesses the strengths of a multi-objective approach, enabling the simultaneous optimization of prediction accuracy and model complexity. To further enhance efficiency, we seamlessly integrate GPU parallelization techniques, significantly expediting both the optimization process and the training of Artificial Neural Networks (ANNs) employed for prediction. Preliminary results demonstrate a remarkable improvement in the accuracy of AGV energy consumption predictions, directly attributed to the synergistic effect of optimizing the ANN architecture and parameters through our proposed PSO framework. This tailored PSO adaptation distinguishes itself by its ability to tackle the complex multi-objective nature of AGV energy prediction with enhanced efficiency and precision. It thus emerges as a compelling and novel solution within the realm of Machine Learning and heuristic methods for agricultural robotics, paving the way for sustainable and optimal AGV operations.

Recovery of iron from copper slag using jatropha oil as a green reductant: Investigation of pyrolysis analysis, reaction kinetic and artificial neural network model prediction

Jatropha oil (JO) is a non-edible vegetable oil and a potential renewable energy source. In this paper, JO was proposed as a reductant to recover iron from copper slag (CS), and the effects of JO dosage, reduction temperature and reduction time on the recovery of iron by deep reduction of CS were investigated. Based on Coats-Redfern method, a thermogravimetric analyzer was used to explore the reactivity, mass loss behavior, and kinetic mechanism of CS reduction by JO, and to compare the reduction characteristics of anthracite and JO char when used as a reductant. Additionally, artificial neural network (ANN) modeling was used to predict the effects of reductant type, temperature, and heating rate on mass loss during CS reduction. The results showed that the optimum condition for CS reduction when JO was used as the reductant was 3 wt% JO at 1250 °C for 45 min, which resulted in 84.97 % Fe recovery and 94.58 % Fe grade in CS; the reduction mechanism of CS followed a one-dimensional diffusion model (D1); and ANN5 (15*1) was the optimum ANN model to predict the reduction of CS. This research introduces innovative strategies for the environmentally friendly and efficient recovery of iron from iron-containing furnace slag.

Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models.

To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET)'s SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets. We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANNs) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo-based algorithms to obtain the joint posterior distributions of the CISNET-CRC models' parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets. The optimal ANN for SimCRC had 4 hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had 1 hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 h for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN. Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, such as the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating 3 realistic CRC individual-level models using a Bayesian approach. We use artificial neural networks (ANNs) to build emulators that surrogate complex individual-based models to reduce the computational burden in the Bayesian calibration process.ANNs showed good performance in emulating the CISNET-CRC microsimulation models, despite having many input parameters and outputs.Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis.This work aims to support health decision scientists who want to quantify the uncertainty of calibrated parameters of computationally intensive simulation models under a Bayesian framework.

Development of ANN-based metaheuristic models for the study of the durability characteristics of high-volume fly ash self-compacting concrete with silica fume

The construction of durable and sustainable infrastructure requires the use of industrial byproducts such as fly ash (FA) and silica fume (SF) to enhance strength and durability. This study introduces novel machine learning models to forecast the results of rapid chloride penetration test (RCPT) for self-compacting concrete (SCC) containing high volumes of FA and SF. This study assessed the effects of supplementary cementitious materials (SCMs) and elevated temperature curing on the RCPT outcomes for SCC. Various metaheuristic algorithms including teaching–learning-based optimization (TLBO), ant colony optimization (ACO), the imperialist competitive algorithm (ICA), and shuffled complex evolution (SCE)—optimize the learning rate, weights, and biases of artificial neural network (ANN) models. A dataset of 360 experimental RCPT data points with seven input parameters was used to train and test the hybrid models. The accuracy of these models was assessed using eight performance indices, and the results were further analyzed through rank analysis, scatter plots, and error matrices. The training and testing sets for the AI models, specifically ANN-TLBO, exhibit a strong correlation between the experimental and predicted RCPT values, with R2 values of 0.9962 for training and 0.9676 for testing, compared to the R2 values of the other proposed models. Consequently, the results of this study suggest that the ANN-TLBO model is the optimal hybrid ANN model for predicting RCPT values in SCC. Verified experimental results and external validation indicate that the ANN-TLBO model is an effective alternative for accurately predicting real-time RCPT in SCC.

ANN model of co-gasification process in bubbling fluidized bed: Contribution ratio analysis

Co-gasification of coal and biomass in a bubbling fluidized bed may offer an effective and economical method to partially replace coal to produce hydrogen, thereby addressing the objectives of achieving a carbon dioxide emissions peak and carbon neutrality. An Artificial neural network (ANN) model was utilized to simulate the hydrogen and other performance of co-gasification in the bubbling fluidized bed. Five types of ANN architectures were tested, including FFBP, CFBP, EFBP, LR, and NARX. Five optimization algorithms were also applied, including L-M, GA, GD, GDX, and PSO, to identify the optimal model to give a more accurate predicate the hydrogen production. The input data were derived from the ultimate analysis of the mixed coal and biomass, the proximate analyzes of mixed coal and biomass, and the operational conditions. The output data consisted of gas composition, carbon conversion, gasification efficiency, total gas yield, syngas yield, low and high heating values of the gas. The LR-PSO with 6 hidden neurons (R2 = 0.9995 and MSE = 0.1357) outperformed other ANN types and optimization algorithms. To further understand the performance of co-gasification, the influences of biomass to coal ratio, equivalence ratio, steam to carbon ratio and temperature on ratio of H2 to CO and syngas yield were predicted. The highest ratio of H2 to CO for the work of Li et al. (2010) [26], Song et al. (2013) [27] and Valdés et al. (2015) [28] were 0.7, 1.2 and 0.88, respectively. And those for syngas yield were 0.85, 1.09 and 1.55 m3/kg, respectively. To reveal the important factor on the ratio of H2 to CO, a detailed analyzed of the contribution ratio was conducted. The most critical factor affecting co-gasification performance in the work of Song et al. (2013) [27] were identified as SC ratio and temperature, which was higher than those of W and ER. The effect of SC exceeded that of T at SC ratio of 0.4, while it came to a reverse at temperature above 700 °C. These findings may serve as a valuable tool for adjusting the various factors to achieve the desired performance in the co-gasification of coal and biomass.

Genetic Algorithm-Based Optimization of Artificial Neural Network of Process Parameters and Characterization of Machining Errors in Graphene Mixed Dielectric

Genetic Algorithm-Based Optimization of Artificial Neural Network of Process Parameters and Characterization of Machining Errors in Graphene Mixed Dielectric

Artificial Intelligence-Powered Optimization and Milk Permeate Upcycling for Innovative Sesame Milk with Enhanced Probiotic Viability and Sensory Appeal.

Consumer demand for plant-based alternatives drives innovation in nondairy beverages. This study explores the development of a novel sesame milk with enhanced functionality using an artificial neural network (ANN) and milk permeate integration. An ANN model effectively optimized water-based sesame milk (WSM) extraction, maximizing total solids (T.S.) recovery. The ANN model's predicted T.S. yield (99.65%) closely matched the actual value (95.18%), demonstrating its potential for optimizing high-yield production. Furthermore, milk permeate was incorporated (5:1 ratio) to create permeate-based sesame milk (PSM), which supported the growth of lactic acid bacteria, suggesting its potential as a growth medium for future probiotic applications. PSM also displayed superior nutritional value and sensory characteristics compared to WSM. These findings highlight the promise of ANN-powered optimization and milk permeate integration for creating innovative sesame milk alternatives with enhanced probiotic viability and sensory appeal. Future research should focus on ANN optimization of alternative-based-plant milk, including permeate-based sesame milk production, the health benefits of LAB fermentation, and consumer preferences for flavors and textures. Optimizing fermentation and LAB selection remain key for commercial success.

Enhancing tribological performance of hybrid fiber-reinforced composites through machine learning and response surface methodology

This study delves into the significant effects of sodium hydroxide (NaOH) treatment on the tribological properties of hybrid fiber-reinforced composites, specifically focusing on the combination of paddy straw (PS) and pineapple leaf (PALF) in a polyester matrix. By leveraging Artificial Neural Networks (ANNs) to predict the Specific Wear Rate (SWR) and Coefficient of Friction (COF), the research employs a grid search approach for hyperparameter optimization. This optimization process results in an optimal ANN architecture with impressive accuracy, showcasing low mean absolute error and high R-squared values of 0.991 and 0.986 for SWR and COF predictions, respectively. Utilizing the Design of Experiments (DOE), the study systematically analyzes the intricate interplay of disc speed, wear duration, and NaOH treatment percentage, with a specific focus on SWR and COF as pivotal tribological metrics. The Analysis of Variance (ANOVA) results underscore the substantial impact of duration and treatment percentage on wear characteristics. Additionally, quadratic regression models reveal nuanced correlations, highlighting the sensitivity of SWR to NaOH percentage and the influence of disc speed, duration, and treatment percentage on COF. This outcome emphasizes the efficacy of these parameters in achieving superior tribological performance in hybrid composites. Beyond contributing to a profound understanding of wear characteristics, this work introduces an innovative dimension through optimized ANN modeling, ensuring a more accurate and fine-tuned predictive model.

An experimental study on enhancing performance and reducing emissions of CRDI engine operated on Co-pyrolysis oil using particle swarm optimization

This study explores the use of Artificial Neural Network and Particle Swarm Optimization to improve the performance and emissions of a CRDI engine fuelled by a mixture of co-pyrolyzed oil and diesel. Experimental results show that the mixed fuels perform better than pure diesel in various engine conditions, achieving higher brake thermal efficiency and lower smoke emissions. The blend D90P10 has the highest BTE of 37.4% working at FIT 30°bTDC and FIP 500 bar. However, NO and CO2 emissions were increased for the same condition. A multi-parametric ANN model is created to build a predictive model based on this experimental data, relating inputs (fuel injection pressure, fuel injection timing, and blend percentages) to outputs (Brake Specific Fuel Consumption, NO, smoke, CO2 emissions). The ANN model attains high accuracy with R2 values of 0.99, 0.98, 0.99, and 0.99 for BSFC, NO, Smoke, and CO2 emission predictions, respectively. The results indicate that for optimal performance, the best conditions are a FIT of 29.71ºbTDC, a FIP of 500 bar, and a co-pyrolysis oil blend percentage of 19.61%. On the other hand, to minimize engine emissions, the best conditions are FIT of 20.28ºbTDC, FIP of 300 bars, and a blend percentage of 25.6%. A balanced approach results in an optimal condition of FIT at 29.5ºbTDC, FIP of 500 bars, and a blend percentage of 6.11%. Experimental validation of these optimal predictions shows a maximum error of only 3.18%, highlighting the reliability and accuracy of the model.

Rapid reactivation of long-term frozen preservation anammox granular sludge by biochar: Performance, mechanism and artificial neural network optimization

Anaerobic ammonia oxidation (Anammox) is an efficient nitrogen removal technology for wastewater, and has been successfully applied in practical wastewater treatment. The preservation and rapid reactivation of anammox bacteria (AnAOB) play an important role in the anammox application. Biochar has been widely used in the water treatment industry due to its stable structure, economic and environmental protection characteristics. Hence, this study used biochar to accelerate anammox granular sludge (AnGS) performance recovery after 60 d preservation under low-temperature frozen (−20 °C). The time of AnGS recovery in the group with biochar addition was shortened by 15 d than the natural recovery group. Meanwhile, biochar addition improved the shock loading resistance of the anammox system. EPS secretion, and the types and quantities of functional groups in EPS increased under biochar stimulation, especially tighter PN. The functional groups in biochar can act as electron shuttles during anammox process, accelerating the electron transfer rate. Biochar addition enhanced the relative abundance of AnAOB, especially Candidatus Brocadia. PICRUSt analysis indicated that biochar promoted key gene related to anammox process and accelerated the recovery of frozen sludge. In addition, the changes in effluent nitrogen removal efficiency were successfully predicted by artificial neural network modeling with R = 0.9895 and MSE = 0.0048. This result may provide a new technological idea for the rapid recovery of AnGS under long time frozen conditions.

Enhancing a Deep Learning Model for the Steam Reforming Process Using Data Augmentation Techniques

Methane steam reforming is the foremost method for hydrogen production, and it has been studied through experiments and diverse computational models to enhance its energy efficiency. This study focuses on employing an artificial neural network as a model of the methane steam reforming process. The proposed data-driven model predicts the output mixture’s composition based on reactor operating conditions, such as the temperature, steam-to-methane ratio, nitrogen-to-methane ratio, methane flow, and nickel catalyst mass. The network, a feedforward type, underwent training with a comprehensive dataset augmentation strategy that augments the primary experimental dataset through interpolation and theoretical simulations of the process, ensuring a robust model training phase. Additionally, it introduces weights to evaluate the relative significance of different data categories (experimental, interpolated, and theoretical) within the dataset. The optimal artificial neural network architecture was determined by evaluating various configurations, with the aim of minimizing the mean squared error (0.00022) and maximizing the Pearson correlation coefficient (0.97) and Spearman correlation coefficient (1.00).

Optimizing reverse osmosis desalination from brackish waters: Predictive approach employing response surface methodology and artificial neural network models

A mathematical model frame has been developed to optimize the performance of reverse osmosis (RO) desalination in brackish waters with medium to high salt concentrations using response surface methodology (RSM) and artificial neural network (ANN), specifically catered towards efficiently operating RO desalination systems. The RSM model aims at predicting RO performance with second-order polynomials based on 15 experimental datasets. Inputs for RSM model include feed TDS concentration, feed water flow rate, and concentration-to-desalination ratio, while the outputs consider rejection, membrane flux, performance index, and specific energy consumption. The Genetic Algorithm optimized back-propagation Neural Network was employed to update network weights and biases, leading to an optimal ANN network structure of 3–10-4. A ratio of 70 % training, 15 % validation, and 15 % test data was chosen to establish an ANN model that predicts the performance of RO desalination. The ANN model demonstrated significantly higher prediction accuracy than the RSM model, with impressive correlation coefficients of 0.9936 for the performance index and 0.9932 for SEC prediction, and low RMSE of 0.85 for rejection. To maximize the performance index and minimize specific energy consumption, a two-step optimization method was further set up by applying the RSM model to initially determine optimal process conditions followed by the ANN model to enhance the accuracy of performance forecasts. Optimal process conditions were proposed for specific feed TDS scenarios, which involved setting a feed TDS concentration limit to meet drinking water standards.

RSM versus ANN for modeling and optimization of magnetic adsorbent based on montmorillonite and CoFe2O4

A highly resourceful, environmentally benign, and recyclable magnetic montmorillonite composite (MMT/CF) was obtained through a simple one-step hydrothermal method and exhibited excellent Pb (II) removal. The as-synthesized adsorbent was then characterized by XRD, SEM–EDX, FTIR, BET, and TGA-DTA. The operating parameters including adsorbent dosage, initial Pb (II) concentration, solution pH, and time were studied. Also, a comparative approach was formed between response surface methodology (RSM) and artificial neural network (ANN) to optimize and model the removal efficiency of Pb (II) by MMT/CF. The results indicated that the ANN model was more precise and quite trusted optimization tool than RSM in consideration of its higher correlation coefficient (R2 = 0.998) and lower prediction errors (RMSE = 0.851 and ADD = 0.505). Langmuir isotherm provided the best fit to the experimental data, and the maximum adsorption capacity was 101.01 mg/g. Additionally, the kinetic studies showed that the pseudo-second-order model fitted well with the experimental data. The magnetic MMT/CF composite possesses high adsorption capacity and is suitable for reuse. Therefore, this study shows that MMT/CF composite can be a potential adsorbent in Pb (II) uptake from aqueous media.

Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach.

Deep eutectic solvents (DESs) are gaining recognition as environmentally friendly solvent alternatives for diverse chemical processes. Yet, designing DESs tailored to specific applications is a resource-intensive task, which requires an accurate estimation of their physicochemical properties. Among them, viscosity is crucial, as it often dictates a DES's suitability as a solvent. In this study, an artificial neural network (ANN) is introduced to accurately describe the viscosity of DESs and their mixtures with cosolvents. The ANN utilizes molecular parameters derived from σ-profiles, computed using the conductor-like screening model for the real solvent segment activity coefficient (COSMO-SAC). The data set comprises 1891 experimental viscosity measurements for 48 DESs based on choline chloride, encompassing 279 different compositions, along with 1618 data points of DES mixtures with cosolvents as water, methanol, isopropanol, and dimethyl sulfoxide, covering a wide range of viscosity measurements from 0.3862 to 4722 mPa s. The optimal ANN structure for describing the logarithmic viscosity of DESs is configured as 9-19-16-1, achieving an overall average absolute relative deviation of 1.6031%. More importantly, the ANN shows a remarkable extrapolation capacity, as it is capable of predicting the viscosity of systems including solvents (ethanol) and hydrogen bond donors (2,3-butanediol) not considered in the training. The ANN model also demonstrates an extensive applicability domain, covering 94.17% of the entire database. These achievements represent a significant step forward in developing robust, open source, and highly accurate models for DESs using molecular descriptors.

Optimization design of an innovative francis draft tube: Insight into improving operational flexibility

Hydropower has transitioned from base-load generation into an energy regulator in the hydro-wind-solar integrated systems, resulting in prolonged turbine operation under off-design conditions. Inspired by the new requirements of operation mode, an innovative Francis draft tube with inclined conical diffuser is presented to mitigate the pressure fluctuations and fatigue damage induced by flow instabilities. The design is optimized and comprehensively investigated to improve the operational flexibility. Computational fluid dynamics combined with design of experiments, artificial neural network and multi-objective optimization process lead to two optimal designs denoted as Opt-1 and Opt-2 from the Pareto fronts. Corresponding maximum pressure fluctuation amplitude is reduced by 15 % and 20 % compared to the traditional, respectively. An 81 % decrease of the fluctuating pressure recovery for the modified draft tube is obtained, leading to a steadier runner torque and consequently lower power swing. The asymmetric design of inclined daft tube is mainly targeted at the mitigation of the synchronous component as well as the attenuation of interaction between the swirling flow and elbow, accompanied with the mitigated energy of first-order vortex structure. The proposed innovative draft tube and optimization technique are found useful to broaden the operating range of hydraulic turbine in a computationally affordable way.

Three-dimensional simulation and artificial neural network optimization of a flat plate heat pipe for the cooling of telecommunication equipment

To address the significantly increased heat dissipation requirements of 5G base stations, a flat plate heat pipe (FPHP) of 500 × 200 × 3 mm3 comprising of 19 independent channels was developed. To solve the problem of simulation distortion due to the high superheat of the initial boiling of the working fluid in the ultra-long channel of 490 × 5 × 1.5 mm3, this paper adopts the Lee model considering the superheat, and simulates based on the Volume of fluid method, and the simulation results are compared with the experiments, and the average relative error is less than 5 %. The FPHP was explored at 100–500 W heat power and filling ratio of 30 %-70 %, depending on the application scenarios of the base station. By partitioning the 19 channels and calculating the thermal resistance of the FPHP based, an Artificial neural network (ANN) model with 10 hidden layer nodes is established based on 675 data sets. The results showed that the mean square error and correlation coefficient were 1.30 × 10-5 and 0.9990. The ANN prediction results are validated, the mean relative error is 5.4 %, demonstrating that the ANN model can be an effective tool to optimize the FPHP for the telecommunication equipment.

An optimal framework for natural English translate processing: On the application of artificial neural network and Adam optimization algorithm

Traditional rule-based and statistical methods have limitations when dealing with complex language structures and semantics. In neural network machine translation algorithms, the objective function is usually to improve the accuracy of n-ary words. However, this does not guarantee a more natural and accurate translation. To overcome these challenges, this paper proposes an optimization algorithm for English natural translation processing based on neural networks, which combines Generative Adversarial Network (GAN) and Transformer models. In GAN, the generative model uses the Transformer model to generate false samples, while the discriminative model uses a binary classifier based on convolutional neural networks and attention mechanisms to distinguish between true and false samples. During the training process, reinforcement learning algorithms are added to evaluate and adjust the generated sentences, and the parameters of the generated model are updated. The classification results of the discriminative model are used together with the Bilingual Evaluation Basis Value (BLEU) objective function to evaluate false samples, and the results are fed back to the generating model to guide parameter updates and optimization. Extensive experiments were conducted on a standard English-Chinese machine translation dataset to evaluate our method. Compared with the benchmark model that only uses supervised learning methods, our neural network-based optimization algorithm for English natural translation processing has achieved significant improvements in translation quality. According to statistical comparison, compared with the Transformer model (BLUE = 33.63 and AP = 90%) and the deep learning model based on long-term and short-term memory (BLUE = 30.26 and AP = 83%), the GAN and Transformer models proposed as the best framework exhibit better performance in bilingual evaluation deficiency (BLEU) (34.35) and accuracy (AP = 95%).

A Two-stage Method for Damage Detection in Z24 Bridge Based on K-nearest Neighbor and Artificial Neural Network

In this paper, we propose an effective approach for identifying damages in the Z24 bridge, a large-scale bridge in Switzerland. The dataset of the Z24 bridge is evaluated as a benchmark, reflecting the behavior of a real structure that has been used for numerous studies. However, most of the previous studies have only addressed the issues of updating the model or estimating the location and severity of damages. The core idea behind our proposed approach is to leverage the strengths of two effective Machine Learning (ML) algorithms: K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN), to assess both the location and severity of damages in the Z24 bridge. First, we employ KNN, an unsupervised learning algorithm, for pinpointing the damage location. This strategy proves highly efficient, significantly reducing computation time by circumventing the need for a loss function during KNN training. By adopting this approach, KNN effectively mitigates the risk of encountering local minima in the ANN optimization process. Subsequently, we deploy ANN to determine the damage severity. When compared to previous studies on the Z24 bridge, our proposed method (KNN-ANN) exhibits promising results. Furthermore, our results illustrate that KNN-ANN consistently outperforms traditional ANN methodologies.