Multilayer Neural Network Model Research Articles

Study on hydroturbine power trend prediction based on machine learning

The application of computational fluid dynamics combined with 3D modeling of the hydraulic model was discussed in this paper. Through the complex calculation of the internal low field, dynamic simulation was used to predict the characteristics of water energy index. Based on the collection of a large number of different types of turbine operation data in different working conditions, the improved multi-layer neural network power prediction model based on the adaptive inverse normalization strategy and the improved grey wolf algorithm were used to optimize the least squares support vector machine power prediction model. Rlue activation function was used in the improved multi-layer neural network model uses to improve the computation speed and prevent gradient disappearance. Combined with L2 regularization to prevent data overfitting, Adam gradient optimizer was applied to achieve fast convergence of network output. The simulation results shown that the method can realize the high precision prediction of turbine output power.

Pressure drop for subcooled water boiling in microchannels under high heat fluxes: Experiments and predictions from artificial neural networks

Subcooled flow boiling in microchannels holds considerable promise in compact cooling applications. This study reports experimental measurements of the pressure drop of subcooled flow boiling of water in microchannels with inner diameters ranging from 0.5875 mm to 2 mm. The experiments are conducted over a range of operating parameters, including q = 3.0–10 MW/m2, p = 3.0–5.0 MPa, and G = 2000–10,000 kg/(m2∙s). The influences of diameter, heated length, and operating parameters are comprehensively analyzed. The results indicate that an increase in heat flux and pressure leads to an advancement of the pressure drop under the same thermodynamic vapor quality. Furthermore, a multi-layer artificial neural networks model optimized by a genetic algorithm is established using over 1000 experimental data. This model is employed to predict the subcooled boiling pressure drop and determine the optimal dimensionless parameter combination for developing a new pressure drop empirical correlation. The optimal combination of parameters includes Ja, ρl/ρg, Lsb/Lsat, Co and We, and the new correlation is proposed to calculate subcooled boiling pressure drop for high-heat-flux and microchannels applications, exhibiting a mean absolute error (MAE) of 10.64%. The results provide a novel approach that integrates machine learning techniques with empirical correlation, aiming to meet the requirements of the design optimization for thermal management solutions.

Determination of future land use changes using remote sensing imagery and artificial neural network algorithm: A case study of Davao City, Philippines

Land use and land cover (LULC) changes refer to alterations in land use or physical characteristics. These changes can be caused by human activities, such as urbanization, agriculture, and resource extraction, as well as natural phenomena, for example, erosion and climate change. LULC changes significantly impact ecosystem services, biodiversity, and human welfare. In this study, LULC changes in Davao City, Philippines, were simulated, predicted, and projected using a multilayer perception artificial neural network (MLP-ANN) model. The MLP-ANN model was employed to analyze the impact of elevation and proximity to road networks (i.e., exploratory maps) on changes in LULC from 2017 to 2021. The predicted 2021 LULC map shows a high correlation to the actual LULC map of 2021, with a kappa index of 0.91 and a 96.68% accuracy. The MLP-ANN model was applied to project LULC changes in the future (i.e., 2030 and 2050). The results suggest that in 2030, the built-up area and trees are increasing by 4.50% and 2.31%, respectively. Unfortunately, water will decrease by up to 0.34%, and crops is about to decrease by approximately 3.25%. In the year 2050, the built-up area will continue to increase to 6.89%, while water and crops will decrease by 0.53% and 3.32%, respectively. Overall, the results show that anthropogenic activities influence the land's alterations. Moreover, the study illustrates how machine learning models can generate a reliable future scenario of land usage changes.

A ppb-level online detection system for gas concentrations in CS2/SO2 mixtures based on UV-DOAS combined with VMD-CNN-TL model

Carbon disulfide (CS2) and sulfur dioxide (SO2) are the main decomposition products of sulfur hexafluoride (SF6). Detecting the concentrations of these products plays a key role in early fault warning in SF6 electrical equipment. In this study, we report an online ppb-level detection system for a mixture of CS2 and SO2 based on ultraviolet differential optical absorption spectroscopy (UV-DOAS) combined with VMD-CNN-TL model. First, important features are extracted and recombined from the differential absorption spectra by variational modal decomposition (VMD) to obtain the combined differential absorption spectra (CDAS) data and mixed-gas differential absorption spectra (MDAS) data. A multilayer 1D convolutional neural network (CNN) model is then pre-trained on CDAS. Next, the parameters of the model are fine-tuned using transfer learning (TL) based on MDAS. TL solves the small sample size problem in detecting mixed-gas concentrations using neural network models. Lab-based results indicate that the system enables stable detection of CS2 (3.81–179.24 ppb) and SO2 (19.21–942.73 ppb) with mean absolute errors of 0.51 ppb and 1.12 ppb, respectively, which are the best results reported so far. Furthermore, the feasibility of the sensor system is verified for detection of gas mixtures at ppb-level by field testing.

A combined scheme of parallel-reaction kinetic model and multi-layer artificial neural network model on pyrolysis of Reed Canary

A comprehensive understanding of pyrolysis kinetics is crucial for the design of biomass pyrolysis. In this study, a combined scheme was proposed for biomass pyrolysis. A kinetic model, serving as a knowledge-based model, was established to capture the primary trend of biomass pyrolysis. An ANN model, as an experience-based model, was developed to provide “details” on the process. The kinetic model incorporated a least-square optimization model to calculate optimal kinetic parameters for pyrolysis reaction steps. The ANN model utilized metaheuristic algorithms such as Particle Swarm Optimization, Pattern Search, Genetic Algorithms, and Surrogate Optimization to optimize the hyperparameters of the ANN model. By combining the kinetic model with the ANN model, comprehensive predictions of biomass pyrolysis were obtained, and the combined Kinetic-ANN model was validated with experimental data at 4 heating rates. Finally, the combined model was compared with the solo-kinetic and solo-ANN models, confirming the advanced performance of the combined approach.

An approach towards building robust neural networks models using multilayer perceptron through experimentation on different photovoltaic thermal systems

The study is aimed at training an artificial neural network to determine the specific Photo voltaic thermal (PV/T) system that will function optimally in a specific environmental situation by optimizing the input parameters. In this regard, four different PV/T systems based on the arrangement of the tubes and the presence of the copper absorber sheet, namely, vertically oscillating tube, vertically oscillating sheet tube, rectangular spiral tube and horizontally oscillating tube PV/T’s were considered. In the experimental study, wind velocity, solar irradiation, ambient temperature, relative humidity, and inlet temperature to the PV/T are considered the influencing factors. The response factors for evaluating the performance of the PV/T system were temperature outlet and power output. Water was used as the working fluid for the thermal management of the systems for all four test cases. The investigation discloses that the rectangular spiral tube photovoltaic thermal system is the best among the studied systems as it maintains both average thermal efficiency and average electrical efficiency at 50.7% and 12.15%, respectively, considering the trade-off between electrical and thermal efficiency. The ideal design can then be selected for a specific area that will be more appropriate with its environmental fluctuations by approximating the production for each scenario. The artificial neural network was designed to be capable of being successfully trained for this application based on Artificial Neural Network (ANN), Multilayer Perceptron using Levenberg Marquardt learning algorithm through MATLAB software based on the experimental data. The ANN model for the rectangular spiral tube PV/T system achieved an exceptional overall R-value of 0.99843 among the proposed prediction models. For the artificial neural network model formulated for vertically oscillating tube, vertically oscillating sheet tube and horizontally oscillating tube PV/T system, the R-value was found to be 0.99807, 0.9943, and 0.99593, respectively. In the training phase, the MSE for vertically oscillating tube, vertically oscillating sheet tube, rectangular spiral tube and horizontally oscillating tube PV/T’s using the multilayer perceptron artificial neural network models were found to be 1.1328, 3.5082, 0.3134 and 1.1328, respectively. For the same PV/T’s models, the MSE values for Cross validation phase were found to be 1.8497, 3.9072, 5.1419, and 4.6402, respectively. Finally, the suggested linear prediction models assist in quickly and easily determining the ideal conditions for every solar system while lowering the error in furcating future results.

Визначення сили впливу іонного променя на об’єкт космічного сміття за контурами його зображень з використанням глибокого навчання

The goal of this article is to develop an effective image preprocessing algorithm and a neural network model for determining the force to be transmitted to a space debris object (SDO) for its non-contact deorbit. In the development and study of the algorithm, use was made of methods of theoretical mechanics, machine learning, computer vision, and computer simulation. The force is determined using a photo taken by an onboard camera. To increase the efficiency of the neural network, an algorithm was developed for feature recognition by the SDO edge in the photo. The algorithm, on the one hand, selects a sufficient number of features to describe the properties of the figure and, on the other hand, significantly reduces the amount of data at the neural network input. A dataset with the features and corresponding reference force values was created for model training. A neural network model was developed to determine the force to be exerted on a SDO from the SDO features. The model was tested using a set of eighteen calculated cases to determine the effectiveness, accuracy, and speed of the algorithm. The proposed algorithm was compared with two existing ones: the method of central projections onto an auxiliary plane and the multilayered neural network model that calculates the force using the SDO orientation parameters. The comparison was performed using the root mean square error, the maximum absolute error, and the maximum relative error. The test results are presented as tables and graphs. The proposed approach makes it possible to develop a system of SDO non-contact removal that does not need to determine the exact relative position and orientation with respect to the active spacecraft. Instead, the algorithm uses camera-taken photos, from which the features necessary for calculation are extracted. This makes it possible to reduce the requirements for its computing elements, to abandon sensors for determining the relative position and orientation, and to reduce the cost of the system.

Enhancing Signature Verification through Neural Network Ensemble

This study presents a signature authentication mechanism to prevent forgery. In the actual world, handling a large collection of data and detecting genuine signatures with reasonable accuracy is often difficult for any verification system. As a result, artificial intelligence techniques are used that can learn from a large data set during the training phase and reply effectively during the application phase without wasting a lot of storage memory space or processing time. It should also be able to refresh its expertise based on real-world encounters on a regular basis. A Multi-Layered Neural Network Model is one such adaptive machine learning technique that is used in this study. Initially, a massive amount of data is gathered by photographing several authentic and fake signatures. The image quality is increased by applying image processing, which is followed by the feature extraction phase, which extracts specific unique standard statistical features.

Construction of Sports Management System Based on Multi-Layer Neural Network Model

Abstract Sports administrators must do an excellent job of preparation before the game. The goal is to keep the game safe. This paper uses the BP neural network to establish the danger warning system for sports competitions. The aim is to reduce the probability of dangerous incidents in sports competitions. Then this paper determines the relevant evaluation indexes of the risk warning mode of sports competition activities. Finally, this paper makes an empirical study of the risk warning model of sports events. The results show that the risk prediction of sports matches based on the BP neural network is accurate.

Estimation and prediction of temperature in Iraq using the multi-layered neural network model

Estimation and prediction of temperature in Iraq using the multi-layered neural network model

Study on the Optimal Double-Layer Electrode for a Non-Aqueous Vanadium-Iron Redox Flow Battery Using a Machine Learning Model Coupled with Genetic Algorithm

To boost the operational performance of a non-aqueous DES electrolyte-based vanadium-iron redox flow battery (RFB), our previous work proposed a double-layer porous electrode spliced by carbon paper and graphite felt. However, this electrode’s architecture still needs to be further optimized under different operational conditions. Hence, this paper proposes a multi-layer artificial neural network (ANN) model to predict the relationship between vanadium-iron RFB’s performance and double-layer electrode structural characteristics. A training dataset of ANN is generated by three-dimensional finite-element numerical simulations of the galvanostatic discharging process. In addition, a genetic algorithm (GA) is coupled to an ANN regression training process for optimizing the model parameters to elevate the accuracy of ANN prediction. The novelty of this work lies in this modified optimal method of a double-layer electrode for non-aqueous RFB driven by a machine learning (ML) model coupled with GA. The comparative result shows that the ML model reaches a satisfactory predictive accuracy, and the mean square error of this model is lower than other popular ML regression models. Based on the known region of operating conditions, the obtained results prove that this well-trained ML algorithm can be used to estimate whether a double-layer electrode should be applied to a non-aqueous vanadium-iron RFB and determine an appropriate thickness ratio for this double-layer electrode.

Development and Validation of a Radiomics Model Based on 3-Dimensional Endoanal Rectal Ultrasound of Rectal Cancer for Predicting Lymph Node Metastasis.

Development of a radiomics model for predicting lymph node metastasis status in rectal cancer patients based on 3-dimensional endoanal rectal ultrasound images. This study retrospectively included 79 patients (41 with lymph node metastasis positive and 38 with lymph node metastasis negative) diagnosed with rectal cancer in our hospital from January 2018 to February 2022. The tumor's region of interest is first delineated by radiologists, from which radiomics features are extracted. Radiomics features were then selected by independent samples t-test, correlation coefficient analysis between features, and least absolute shrinkage and regression with selection operator. Finally, a multilayer neural network model is developed using the selected radiomics features, and nested cross-validation is performed on it. These models were validated by assessing their diagnostic performance and comparing the areas under the curve and recall rate curve in the test set. The areas under the curve of radiologist was 0.662 and the F1 score was 0.632. Thirty-four radiomics features were significantly associated with lymph node metastasis (P < .05), and 10 features were finally selected for developing multilayer neural network models. The areas under the curve of the multilayer neural network models were 0.787, 0.761, 0.853, and the mean areas under the curve was 0.800. The F1 scores of the multilayer neural network models were 0.738, 0.740, and 0.818, and the mean F1 score was 0.771. Radiomics models based on 3-dimensional endoanal rectal ultrasound can be used to identify lymph node metastasis status in rectal cancer patient with good diagnostic performance.

Identification of paddy blast disease field images using multi-layer CNN models.

Farmers and agricultural experts can take action on many areas of paddy crop handling and management practices with the use of actionable information from the in-field diagnosis of paddy blast disease. To successfully diagnose the blast disease affecting fifteen different paddy crop varieties, three transfer learning multi-layer convolutional neural network (CNN) models, such as CapsNet, EfficientNet-B7, and ResNet-50, are presented in this paper. The field images of blast disease are captured and classified based on disease severity levels, such as low, medium, high, and severe. The study employing the CapsNet model with adataset consisting of a total of 20,000 labeled images demonstrated significant results with a testing efficiency of 90.79% and a validation efficiency of 93.29%. The ResNet-50 and EfficientNet-B7 models have yielded average testing efficiencies of 85.10% and 88.72%, respectively. On the held out blast disease affected paddy field image dataset, the CapsNet model outperformed the EfficientNet-B7 and ResNet-50 CNN models in terms of both classification efficiency and computational efficiency.

Reliability study of generalized Rayleigh distribution based on inverse power law using artificial neural network with Bayesian regularization

Using the generalized Rayleigh distribution and the inverse power law, this paper proposes a new reliability model and investigates the effect of the key parameters on reliability measurements. This proposed new model offers a more accurate way to model the performance of electronic components over their lifetimes. In order to analyze the reliability parameters, a multi-layer artificial neural network model has been developed by using the datasets generated by numerical methods and obtained in four different scenarios. Using the artificial neural network model with 5 neurons in the hidden layer, the reliability parameters Hazard Rate Function, Odds function, Reversed Hazard Rate Function, Mean Time to Failure and Mean Time Between Failures have been estimated. The results obtained have been analyzed comprehensively and explained with graphics. The study findings showed that there was a direct relationship between the reliability parameters examined in all scenarios and an increase in the Mean Time Between Failures value appeared for each scenario. However, it has also been seen that the developed artificial neural network can make predictions with very high accuracy and is a powerful engineering tool that can be utilized in reliability analysis.

Membership-Mismatched Impulsive Exponential Stabilization for Fuzzy Unconstrained Multilayer Neural Networks With Node-Dependent Delays

This article focuses on the membership-mismatched impulsive exponential stabilization for fuzzy unconstrained multilayer neural networks (MNNs) with node-dependent time-varying delays (NDTVDs). To begin with, this work proposes a novel MNNs model with unconstrained interlayer and intralayer parameters, which may allow nodes in all layers to have inconsistent attributes and structures. Meanwhile, the novel model considers the NDTVDs and removes the strict constraints including node alignment and one-to-one interlayer connection to meet diverse modeling requirements in complex applications. Then, the proposed fuzzy impulsive controller does not need to share the same fuzzy parameters as the fuzzy MNNs model, reducing the implementation complexity of the fuzzy impulsive controller. To derive the main results using the augmented vector form of unconstrained MNNs, the sparse matrix method is proposed to convert the node-dependent delayed MNNs model into an equivalent model with multiple delays. Moreover, the time-dependent Lyapunov function (TDLF) technique is adopted to improve the reliability of the stabilization conditions by fully utilizing the state information of both the current and neighboring impulsive intervals. Finally, the main results are verified using numerical simulation.

Urban development modeling in Al Diwaniyah province of Iraq using RS-GIS

The rapid growth of the population and the consequent increase in migration to the cities during the past decades have caused the expansion of many urban areas. In this study, the growth and development of the urban areas of Al-Diwaniyeh during the recent decades using three categories: 1. urban limits layer extracted from land use maps during the period, 2. population density layers and 3. Degree of urbanization in the period 1975 to 2015 It was investigated, monitored and modeled. Modeling and forecasting for the future was done using layers of population density and degree of urbanization. The population density map for 2025 was implemented based on population changes in the last two decades using the multilayer artificial neural network method. While the prediction of the degree of urbanization was operationalized by creating and applying a multi-layer neural network model for 2030. The results of the study showed that the general trend of the expansion of Al-Diwaniye city limits during the period of 1975 to 205 was increasing. The degree of urbanization in Al Diwaniyah has been increasing since 1975. After 1975, the initial cores of the dense areas were formed, and gradually, in the following periods, the expansion of the urban areas of Al-Diwaniyeh was observed around the cores. The class of rural areas has shown an upward trend with a more uniform slope compared to other classes of the degree of urbanization. While after 1990, high density and low density classes with close areas have also gone through an upward trend. The rate of population density and, accordingly, the rate of population in Al-Diwaniye has been increasing between 1975 and 2015. The trend of maximum population changes has been downward; therefore, it can be claimed that the initial dense cores of the urban population of Al-Diwaniyeh have moved to the surrounding areas. In the estimated degree of urbanization in 2030, the class with high density has faced a decrease in area compared to the previous period. In general, the area of ​​built-up areas in 2030 will increase compared to previous periods, but this will be achieved with a slower acceleration and slope compared to previous periods.

Application of Artificial Neural Network Models to Differentiate Between Complicated and Uncomplicated Acute Appendicitis

Preoperative prediction of complicated appendicitis is challenging, and many clinical tools are developed to predict complicated appendicitis. This study evaluated whether a supervised learning method can recognize complicated appendicitis in emergency department (ED). Consecutive patients with acute appendicitis presenting to the ED were enrolled and included into training and testing datasets at a ratio of 70:30. The multilayer perceptron artificial neural network (ANN) models were trained to perform binary outcome classification between uncomplicated and complicated acute appendicitis. Measures of sensitivity, specificity, positive and negative likelihood ratio (LR + and LR-), and a c statistic of a receiver of operating characteristic curve were used to evaluate an ANN model. The simplest ANN model by Bröker et al. including the C-reactive protein (CRP) and symptom duration as variables achieved a c statistic value of 0.894. The ANN models developed by Avanesov et al. including symptom duration, appendiceal diameter, periappendiceal fluid, extraluminal air, and abscess as variables attained a high diagnostic performance (a c statistic value of 0.949) and good efficiency (sensitivity of 78.6%, specificity of 94.5%, LR + of 14.29, LR- of 0.23 in the testing dataset); and our own model by H.A. Lin et al. including the CRP level, neutrophil-to-lymphocyte ratio, fat-stranding sign, appendicolith, and ascites exhibited high accuracy (c statistic of 0.950) and outstanding efficiency (sensitivity of 85.7%, specificity of 91.7%, LR + of 10.36, LR- of 0.16 in the testing dataset). The ANN models developed by Avanesov et al. and H.A. Lin et al. developed model exhibited a high diagnostic performance.

Modeling Dust Generation on Low-Volume Roads Based on Vehicle Speed and Surface Fines Content

This study analyzes the role of vehicle speed and surface fines content on dust emission. Accordingly, fifty unpaved road sections in Iowa were evaluated; surface loose-aggregate samples were collected, and dust was collected using a Colorado State Dustometer at three speeds: 25 mph, 40 mph, and 55 mph. The data were analyzed using analysis of variance (ANOVA) test. Several dust-prediction models were developed utilizing multiple linear regression (ML), nonlinear regression with an interaction term (NLI), nonlinear beta regression (NLB), nonlinear curve-fitting regression (NLCF), and a multilayer neural network (MNN). The model predictors included vehicle speed and surface fines content. When models were evaluated using synthetic data and compared using post-hoc analysis, it was found that dust increases exponentially as vehicle speed increases and increases linearly as surface fines content increases. Also, at higher speeds, dust values will converge independently of the fines content in the surface materials. The ANOVA test results revealed that vehicle speed, surface fines content, and their interaction significantly affected dust emissions. The accuracy of models ranged from acceptable to good. The coefficients of determination ( R2) for ML, NLI, NLB, NLCF, and MNNTraining models were 0.703, 0.718, 0.689, 0.696, and 0.776, respectively. Evaluation of the models showed that independent of the R2 value, the MNN model was the most accurate in predicting dust emissions, followed by the NLCF model, the ML model, the NLB model, and lastly the NLI model. The post-hoc test showed that MNNTraining, NLCF, and ML models produced comparable results.

3D laser imaging for measuring volumetric shrinkage of horticultural products during drying process

The standard method of shrinkage measurement consists of immersion of the product in a fluid in order to calculate the volume changes before and after drying. It is destructive and time-consuming and also is not a practical method to be used in online drying monitoring systems. To date, it has been tried for measuring shrinkage based on passive stereo vision. But no report has been provided so far on the accuracy of this technique and its comparison with conventional method of measuring volumetric shrinkage. On the other hand, because of the small size of dried foodstuff products, it does not seem that the stereo vision to be able to extract high detail point clouds from the surface of objects. Therefore, this research was conducted in order to study the potential use of 3D laser scanning for measurement of the volumetric shrinkage of some horticultural products during drying process. To this end, a calibrated 3D laser imaging system was applied in order to precisely scan the surface of some small size horticultural products (including plum, fig, date, and button mushroom), which take non-symmetric form during the drying process. 2D image of samples was also taken to predict the volumetric shrinkage by various texture analysis methods. Drying was carried out by a convective dryer. The results indicated a significant superiority of 3D laser imaging compared to 2D imaging. The value of correlation coefficient and mean absolute percentage error of multilayer perceptron artificial neural networks models created based on selected spatial features of point clouds in predicting volumetric shrinkage for plum, fig, date, and mushroom was obtained 0.90 and 19.48, 0.95 and 14.25, 0.78 and 23.54, and 0.87 and 9.47, respectively.

Analyzing and Modeling the Spatial-Temporal Changes and the Impact of GLOTI Index on Precipitation in the Marmara Region of Türkiye

Precipitation is a particularly important part of the Earth’s hydrological cycle and, therefore, is a necessary variable for maintaining natural balance. This study investigated past, present, and future changes in precipitation in the Marmara region, and examined the effects of global warming on this variable. The study period was from 1960 to 2020, and the climate data of 15 synoptic stations in the Marmara region were used for this purpose. To achieve the objectives of the study, linear and 6th order polynomial regression, ombrothermic and hythergraph diagrams, geostatistical models, Mann-Kendall test, Pearson correlation, standard Z-scores, and multi-layer perceptron artificial neural network models (MLP-ANN) were used to model and predict precipitation. The results of the linear regression analysis showed that of the 15 stations, 6 stations had an increasing trend, 6 stations had a trendless pattern, and 3 stations had a decreasing trend. In terms of periodic analysis, the main downward trend started in 1964 and continued until 1992, while the main periodic upward trend started in 1992 and continued until 2016. The synoptic stations in the Marmara region showed a lack of precipitation over six to seven months of the year, and the precipitation changes in the region were stronger than the temperature changes. In addition, the highest precipitation was observed on the southeast coast of the Black Sea, and the lowest precipitation was observed in the eastern parts of the region. Moreover, except for the Bilecik and Kocaeli stations, the changes in the long-term trend of precipitation at the other stations were significant. Among the 15 stations, only the Kocaeli and Sarıyer stations showed a positive correlation with global temperature during the annual period. In addition, the developed ANN model was accurate in simulating and predicting precipitation and showed an upward trend over the next seven years.