Neural Network-Based Approximation Model for Perturbed Orbit Rendezvous

In the present study, deep learning neural network model has been employed in many engineering problems including heat transfer prediction. The main consideration of this document is to predict the performance of the boiling heat transfer in helical coils under terrestrial gravity conditions and compare with actual experimental data. Total of 877 data sample has been used in the present neural model. Artificial new Neural Network (ANN) model developed in Python environment with Multi-layer Perceptron (MLP) using four parameters (helical coils dimensions, mass flow rate, heating power, inlet temperature) and one parameter (outlet temperature) has been used in the input layer and output layer in order. Levenberg-Marquardt (LM) algorithm using L2 Regularization to find out the optimal model. A typical feed-forward neural network model composed of three layers, with 30 numbers of neurons in each hidden layer, has been found as optimal based on statistical error analysis. The 4-30-30-1 neural model predicts the characteristics of the helical coil with the accuracy of 98.16 percent in the training stage and 96.68 percent in the testing stage. The result indicated that the proposed ANN model successfully predicts the heat transfer performance in helical coils and can be applied for others operation concerned with heat transfer prediction for future works

This paper presents an application of the neural network (NN) model for forecasting and filtering problems. Instead of using the gradient descent method as used by the standard back-propagation network, the present study adopted the Levenberg-Marquardt algorithm. It was shown that the Levenberg-Marquardt algorithm tends to reach termination criteria more quickly making the searches employing the full model less time consuming. In the first part of forecasting problem, multilayer feedforward (MLFF) network was constructed by trials to forecast the tidal-level variations at the Chao Phraya river mouth in Thailand. Unlike the well-known conventional harmonic analysis, the neural network model uses a set of previous data for learning and then forecasting directly the time series of tidal levels. It was found that lead time of 1 to 24 hourly tidal levels can be successfully predicted using only a short-time hourly learning data.In the second part of filtering problem, the MLFF network was also used to establish a stage-discharge relationship for tidal river. Comparisons were made among conventional methods, i.e. by using multivariate functions (Linear and power models) and the NN model. The NN model was found to be able to model the transfer function too much higher accuracy than multiple regression analysis. The root mean square errors (RMSE) and the mean relative error (MRE) for the NN model are about 8 cm and 6.4% whereas the same errors for multiple regression analysis are 16 cm and 12%, respectively. The efficiency index for the linear, power, and NN models is 0.4, 0.7, and 0.9, respectively. In addition, the obtained stage-discharge relationship by the NN model can indicate reasonably important behavior of the tidal influences, i.e. there are stronger influences of tide during the dry period than the flood period.

Artificial intelligence-based surrogate modeling for computational cost-effective optimization of hydrogen liquefaction process

Background Urinary tract infections (UTIs) are a common cause of healthcare visits and hospitalizations. A recent study attributed about 8.7 million ER visits to UTIs from 2016 to 20231. Asymptomatic bacteriuria (ASB) is often mistaken for a UTI, leading to unnecessary treatment and the development of resistant bacteria. This study aims to develop a deep learning model to differentiate ASB from a UTI based on urinalysis parameters and to compare its performance to traditional statistical methods, specifically logistic regression.Figure 1.Feedforward neural network model with 3 hidden layers and 2, 128, and 8 nodes in their respective layers.Figure 2.Proportion of ASB (Blue) vs. a UTI (Red) of urinalysis parameters. Methods This retrospective case-control study included adult patients with ASB or UTIs from January 1st, 2010 to December 31st, 2022. Urinalysis parameters and urine culture results were collected. A feedforward neural network with 30 input features and 2 hidden layers was trained and tuned to predict either ASB or a UTI (Figure 1). The model's performance was compared to a stepwise bidirectional logistic regression model. Results 272 patient encounters were included (85 UTI, 187 ASB). The neural network model was found to be the optimal hyperparameter for the model based on the highest validation AUC of 0.69 (Figure 1). The tuned model achieved a binary accuracy of 0.83, precision of 0, and recall of 0 on the validation set with batch size of 4 and learning rate of 0.0003. The testing set showed an AUC of 0.35, binary accuracy of 0.842, precision of 0, and recall of 0 (Figure 2). The stepwise bidirectional logistic regression model, with an AIC of 270.65, demonstrated an accuracy of 0.745, but a precision of 0, a recall of 0, and an AUC of 0.525. Statistically significant parameters (p< 0.01) included red color (estimate: 0.6956), straw color (estimate: -0.292), and densely turbid clarity (estimate: 1.696). Conclusion While the neural network model showed some predictive capabilities, the logistic regression model had higher accuracy. However, it also lacked precision and recall, indicating limitations in its ability to correctly identify UTI cases. Future tests with larger datasets may improve the models' accuracy in differentiating ASB from a UTI, if such patterns exist. Our results highlight the complexity of developing a robust predictive model for differentiating ASB vs. a UTI and the need for continued research on the topic. Disclosures All Authors: No reported disclosures

This paper aims to develop a wind speed prediction model by utilizing deep learning and neural networks. The analysis of weather data using a neural network architecture has been completed. The Long Short-Term Memory (LSTM) architecture is a type of artificial Recurrent Neural Network (RNN) used in deep learning is the first method plots the predicting Wind Speed based on the dataset and predicts the future spread. A dataset from a real-time weather station is used in the implementation model. The dataset consists of information from the weather station implements of the recurrent neural network model that plots the past spread and predicts the future stretch of the weather. The performance of the recurrent neural network model is presented and compared with Adaline neural network, Autoregressive Neural Network (NAR), and Group Method of Data Handling (GMDH). The NAR used three hidden layers. The performance of the model is analyzed by presenting the Wind Speeds of Erbil city. The dataset consists of the Wind Speed of (1992-2020) years, and each year consist of twelve months (from January to December).

In this paper, a reconfiguration approach using fuzzy logic algorithm and neural network modelling is proposed. When failure has been detected, the state of the degraded system is evaluated by comparing the output of the system with the estimation provided by a neural model. Therefore, we propose to use the neural network in order to cover all the operating zone of the faulty system. By combining neural network capabilities and fuzzy logic for fault evaluation, a new control law is determined taking into account the impact of the failure on the system. Its potentialities are illustrated through simulation studies on a thermal plant presenting bilinear characteristics. >

An attempt has been made to develop neural network and neuro-fuzzy based models to predict the green compressive strength of clay bonded moulding sand mixtures and to analyse the properties of the mixed sand. Data for the models were generated following standard experimental procedure in the sand laboratory. Process parameters such as clay content, moisture, coal dust and mulling time were varied. The collected data were used in developing these models. The predicted green compressive strengths by neural network and neuro-fuzzy based models have been found to be in good agreement with the experimental values. Furthermore, it has been observed from the results that the predicted values from the neuro-fuzzy model are more accurate than those predicted from the neural network model.

The accurate calculation of the height of water-flowing fractured zone (WFFZ) in coal mine is a critical factor in ensuring mine safety and protecting surficial eco-environment. In view of inapplicability of traditional empirical formula for predicting the height of WFFZ, the correlation of the height of WFFZ and influence factors was analyzed firstly based on 82 collected groups of coalfield measured data in China. Results show that the mining thickness and mining depth have a significant effect on the height of WFFZ. Subsequently, the measured data were divided into two parts: 80% for training models and the remaining 20% for validation. Two prediction models, i.e., the multiple regression (MR) model and BP neural network (BPNN) model, were established and trained. A new merging model, multiple regression-BP neural network (MR-BPNN) model, was proposed by combining the multiple regression and BP neural network model. The prediction accuracy and generalization ability of the three models were verified by the recorded testing samples. Results of comparison suggest that three models all had better applicability for predicting the height of WFFZ in the coalmine, compared with the existing empirical prediction methods. More importantly, the MR-BPNN merging model combined the nonlinear mapping ability of neural network and empiric of multiple regression model, which could provide high-accurate, strong-generalized, and practical application for predicting the height of WFFZ of coalfield. In addition, the reason for the inapplicability of traditional empirical formula and the practicability of the proposed neural network-based prediction models were discussed.

Background: Data mining (DM) is an approach used in extracting valuable information from environmental processes. This research depicts a DM approach used in extracting some information from influent and effluent wastewater characteristic data of a waste stabilization pond (WSP) in Birjand, a city in Eastern Iran. Methods: Multiple regression (MR) and neural network (NN) models were examined using influent characteristics (pH, Biochemical oxygen demand [BOD 5 ], temperature, chemical oxygen demand [COD], total suspended solids [TSS], total dissolved solid [TDS], electrical conductivity [EC] and turbidity) as the regression input vectors. Models were adjusted to input attributes, effluent BOD 5 (BODout) and COD (CODout). The models performances were estimated by 10-fold external cross-validation. An internal 5-fold cross-validation was also used for the training data set in NN model. The models were compared using regression error characteristic (REC) plot and other statistical measures such as relative absolute error (RAE). Sensitivity analysis was also applied to extract useful knowledge from NN model. Results: NN models (with RAE = 78.71 ± 1.16 for BODout and 83.67 ± 1.35 for CODout) and MR models (with RAE = 84.40% ± 1.07 for BODout and 88.07 ± 0.80 for CODout) indicate different performances and the former was better (P < 0.05) for the prediction of both effluent BOD 5 and COD parameters. For the prediction of CODout the NN model with hidden layer size (H) = 4 and decay factor = 0.75 ± 0.03 presented the best predictive results. For BODout the H and decay factor were found to be 4 and 0.73 ± 0.03, respectively. TDS was found as the most descriptive influent wastewater characteristics for the prediction of the WSP performance. The REC plots confirmed the NN model performance superiority for both BOD and COD effluent prediction. Conclusion: Modeling the performance of WSP systems using NN models along with sensitivity analysis can offer better understanding on exploring the most significant parameters for the prediction of system performance. The findings of this study could build the foundation for prospective work on the characterization of WSP operations and optimization of their performances with a view to conducting statistical approaches.

In this paper, the Direction of Arrival -DoA estimation for two mobile sources was performed by using the Single Multilayer Perceptron (MLP) neural network model (SMLP-DoA) and the Cascade MLP model(CMLP).The latter model consists of two neural networks connected in a cascade where the outputs of the first MLP that rejects noise represent the inputs to the second network in a cascade.The outputs of the neural network models determine the direction of arrival of the incoming signals.Two cases were considered, in the first case the neural networks were trained on the samples that were without noise, and in the second with samples containing noise.Both considered neural network models were tested with noisy samples.The results of these two neural models are compared to the results achieved by the RootMUSIC algorithm.The presented results show that the proposed CMLP model has a higher accuracy in determining the angular positions of sources compared to the classical SMLP-DoA model and the RootMUSIC algorithm.Moreover, the CMLP model executes significantly faster compared to the model based on the RootMUSIC algorithm.

1. In this study, neural network (NN) and response surface (RS) models were developed to investigate the response [average daily gain (ADG) and feed efficiency (FE)] of young broiler chickens to dietary protein and lysine. For this purpose, data on their responses to dietary protein and lysine were extracted from the literature and separate NN and RS models were constructed. 2. Comparison between the NN and RS models revealed higher accuracy of prediction with the NN models compared to the RS models. In terms of R 2 values, the NN models developed for both ADG (R 2 = 0.923) and FE (R 2 = 0.904) were far superior to the RS models (R 2 for ADG = 0.511; R 2 for FE = 0.67). This suggests that the NN models can serve as an alternative option to conventional regression approaches including use of RS models. 3. Optimisation of the NN models developed for response to protein and lysine showed that diets containing 220.7 (g/kg of diet) protein and 12.85 (g/kg of diet) lysine maximise ADG, whereas maximum FE is achieved with diets containing 241.3 and 13.12 (g/kg) protein and lysine, respectively. Based on the optimisation results, optimal dietary protein and lysine concentrations for maximum FE in broiler chickens during the starting period are higher than for ADG.

The catalytic cracking process used for gasoline desulfurization and olefin reduction often results in a reduction of the gasoline's octane rating, which can negatively affect economic outcomes. To address this issue, this study applies data mining techniques to analyze cumulative sample data spanning three years from a petrochemical firm. It utilizes a combination of Support Vector Machine and Recursive Feature Elimination (SVM-RFE) to identify the key variables and reduce data complexity. Additionally, the BAS algorithm is incorporated to optimize the BP neural network model, which is then used to develop a prediction model for gasoline octane loss. The model 's accuracy is evaluated using metrics such as the Root Mean Square Error (RMSE) and Mean Relative Error (E). The findings indicate that the BAS-BP neural network-based prediction model closely aligns with actual values, offering valuable insights for forecasting octane loss during gasoline processing.

Constitutive modeling plays a vital role in simulating the stress-strain behavior of structures within various engineering applications. Traditionally, constitutive models relied on experimental tests to accurately characterize the stress-strain relationship. In recent decades, there has been a significant increase in the development of advanced constitutive laws due to the high cost associated with testing. However, numerical computations based on advanced constitutive laws heavily relies on numerical integration which is time-consuming and computationally demanding. This leads to the need to develop an alternate approach. In this study, a neural network-based constitutive model using TensorFlow was proposed to capture the non-linear relationship between stress and strain in beam structures. A case study was conducted where the neural network-based constitutive model was implemented to predict the axial force and bending moment of the pipeline subjected to ground displacement, modeled as an Euler-Bernoulli beam with large deformations. The comparative analysis with traditional numerical integration schemes was pursued to assess the performance of the proposed model. The results showed a significant reduction in computation time when dealing with large data sizes. Additionally, the impacts of the training data samples on the applicability of the neural network constitutive model were investigated. The findings of this study support that the neural network-based constitutive model serve as a more efficient tool for extensive numerical simulations in the design and structural analysis of beam structures.

This paper reports the radiative transfer simulations for the passive microwave radiometer onboard the proposed Indian climate research satellite Megha-Tropiques due to be launched in 2011. These simulations have been performed by employing an in-house polarized radiative transfer code for raining systems ranging from depression and tropical cyclones to the Indian monsoon. For the sake of validation and completeness, simulations have also been done for the Tropical Rainfall Measuring Mission (TRMM)’s Microwave Imager (TMI) of the highly successful TRMM mission of NASA and JAXA. The paper is essentially divided into two parts: (a) Radiometer response with specific focus on high frequency channels in both the radiometers is discussed in detail with a parametric study of the effect of four hydrometeors (cloud liquid water, cloud ice, precipitating water and precipitating ice) on the brightness temperatures. The results are compared with TMI measurements wherever possible. (b) Development of a neural network-based fast radiative transfer model is elucidated here. The goal is to speed up the computational time involved in the simulation of brightness temperatures, necessitated by the need for quick and online retrieval strategies. The neural network model uses hydrometeor profiles as inputs and simulates spectral microwave brightness temperature at multiple frequencies as output. A huge database is generated by executing the in-house radiative transfer code for seven different cyclones occurred in North Indian Ocean region during the period 2001–2006. A part of the dataset is used to train the network while the remainder is used for testing purposes. For the purpose of testing, a typical scene from the southwest monsoon rain is also considered. The results obtained are very encouraging and show that the neural network is able to mimic the underlying physics of the radiative transfer simulations with a correlation coefficient of over 99%.

Coronary artery disease (CAD) has become very common nowadays and is the leading cause of death across the world. In this paper, an attempt has been made to develop a model for the classification of CAD by using clinical features, ECG, and features from laboratory tests of patients. Optimum features were selected with the help of domain expert advice and random forest (using mean decreasing accuracy). A neural network-based classification model was developed with the selected features for classification of left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA) in human heart. To get the optimum result from the neural network model, a 10-fold cross-validation method was adopted. The optimized model reached its maximum accuracy, specificity, and sensitivity of 91.397, 90.32, and 95% for LAD; 88.09, 80, and 91.52% for LCX and 90.36, 72.72, and 95.71% for RCA classification, respectively.