Artificial Neural Network Output Research Articles

Modeling the Drying Process of Onion Slices Using Artificial Neural Networks

One of the food preservation technologies is the drying process, which requires heat and is significantly energy-intensive, resulting in high costs. This caused the search for new design solutions for dryers, which requires continuous experimental research and the creation of new decision-supporting models for the optimization of drying processes. In this work, four models of the kinetics of convective onion drying were developed using Artificial Neural Networks (ANNs), taking into account pre-treatment before drying and the different temperatures of the drying agent. The moisture content in the dried material at a specific moment in time was taken as the dependent variable (ANN output). The following were accepted as independent variables (ANN inputs): drying temperature, initial sample thickness, initial moisture content, initial mass of the sample, time of drying, and material pre-treatment (no pre-treatment—blanching–osmotic dehydration). Four semantic models were formulated, the general Ann1 model taking into account all input variables and three detailed Ann2 models for individual types of pre-treatment. For the best Ann1, the MAPE values were 5.88–7.02% (for different data: Training, Test, Validation). For the detailed Ann2 models, the error values were more than twice lower. The MAPE values ranged from 1.14% to 3.12%.

Performance prediction for network-level flexible pavements using an optimised ANN approach

ABSTRACT Considering that the road network shares traffic flow as a system, this study aimed to predict the network-level flexible pavement performance, using an optimised artificial neural network (ANN) approach to predict the pavement performance of each individual road, and the Ford-Fulkerson algorithm to determine the weight coefficients of roads. ANNs were developed to predict distress conditions, functional conditions and structural performance using the inputs of the pavement age and structural, traffic and climatic conditions. ANNs were trained with Long-Term Pavement Performance (LTPP) program data, and almost all the coefficient of determination values between ANN outputs and measured results are larger than 0.9. This ANN approach was optimised by establishing grey models to provide predictive short-term performance data for training ANNs, and employing Kalman Filter to modify the long-term performance prediction. Applications in typical LTPP sections validated the effectiveness of the optimisation method. This study used the reduction in road network capacity due to the assumed unavailability of a certain road to quantify its role (weight coefficient), which was calculated using the Ford-Fulkerson algorithm. This network-level performance prediction approach was applied in a 25-expressways network in Shanghai, which validated the good generalisation capabilities of ANNs and feasibility of the Ford-Fulkerson algorithm.

From Detection to Action: Implementing Deep Learning Inference in PLC Systems via Docker Services

This paper focuses on the implementation of recognition and detection algorithms for programmable logic controllers (PLCs) based on artificial intelligence (AI). AI methods based on deep neural networks (DNN) are characterized by a large number of hidden layers and neurons in hidden layers, which increases the computational complexity of computing the outputs of artificial neural networks (ANNs). Popular convolutional neural networks (CNN) are among such ResNet, AlexNet, GoogLeNet and others. A remote cloud solution is proposed in this paper to compute CNN outputs. The CNN implementation was tested on object recognition and detection applications. Training and testing of the CNN and YOLO detector was carried out in the Python environment.

Rule extraction based on PROMETHEE-assisted multi-objective genetic algorithm for generating interpretable neural networks

Rule extraction from artificial neural networks (ANNs) refers to the process of identifying the underlying decision-making logic used by the network in its predictions. This involves converting the complex, non-linear relationships between inputs and outputs in the network into a set of interpretable rules. The main objective of rule extraction is to enhance the interpretability of decision-making in artificial neural networks (ANNs) and make it more understandable for human experts, especially in critical domains like healthcare, where clear decision-making is essential. Despite the ability of machine learning methods to interpret ANNs, interpreting complex neural networks remains limited, as most studies tend to sacrifice one criterion, related to rule quality, in favor of another. Rule extraction methods applied directly to complex neural networks may produce overly specific rules that are tailored to the training data. Such rules can suffer from overfitting and may not generalize well to unseen data. Also, The use of multiobjective genetic algorithms for rule extraction can result in a large number of rules, making it challenging for decision-makers to interpret them. This study proposes an innovative approach to address challenges in rule extraction from complex artificial neural networks (ANNs) by integrating a multiobjective genetic algorithm with the PROMETHEE method. By integrating these techniques, the study aims to identify meaningful relationships between inputs and outputs of ANNs, generating concise and reliable rules that are ranked based on support, confidence, and lift values. The use of PROMETHEE's rule ranking mechanism enabled the consideration of rule priority during candidate selection, guiding the genetic algorithm towards discovering high-quality rule sets and efficiently exploring the search space. This approach not only enhances the quality of extracted rules but also facilitates efficient decision-making by striking a balance between rule accuracy, fidelity, and comprehensibility, thus contributing to advancing the understanding of complex neural networks. By equally weighting the criteria (support, confidence, and lift) during the rule ranking mechanism, our approach achieved high coverage rates ranging from 94.88 % to 100 % and generated a manageable number of rules that could be easily interpreted by human experts, ranging from 5 to 10.5. These findings demonstrate the potential of our approach to significantly improve decision-making accuracy and interpretability across various real-world applications, making it a promising tool for applications in healthcare, finance, and other fields.

Using Fundamental Parameters and Artificial Neural Network for Controlling the Laser Welding of Metals

The present work aims to design an artificial neural network (ANN) for controlling the geometry of the fusion zone in different welding conditions. AA5754 aluminium plates with 6 mm thickness were welded in butt configuration by using an Yb-doped fiber laser in continuous wave regime. Laser power, travel speed, beam diameter and shielding gas were considered as process-related factors, while the weld geometry was evaluated in terms of penetration depth and bead width. The accuracy of the model was endorsed by using untrained test data. Results showed a good agreement between the ANN output and the experimental values.

Modeling and optimization of the corrosion resistance of Cr-free and Cr-based chemical conversion coatings on nickel foil by artificial neural network and response surface method

Chemical conversion treatment is one of the most powerful methods to prevent the corrosion of metals. However, determining the optimal addition amount of various active ingredients for the enhancement of corrosion resistance requires a large number of repeated experiments, which is very time-consuming and is also not helpful for figuring out the interactions between active ingredients. In the present study, Cr-free conversion coatings (CFCC) and Cr-based conversion coatings (CCC) were prepared on nickel foil and characterized by scanning electron microscopy and X-ray photoelectron spectroscopy to determine their morphology and composition of them. The corrosion resistance of conversion coatings was measured by salt spray test and further investigated using the response surface method (RSM) and artificial neural network (ANN) approach. The constructed models displayed high predictive accuracy, and the coefficient of correlation value of the RSM-CFCC, ANN-CFCC, RSM-CCC, and ANN-CCC models is 0.9441, 0.9513, 0.9535 and 0.9564, respectively. This comparative study recommends the ANN model best fits the experimental data for both CFCC and CCC. By using the ANN outputs, satisfactory results can be estimated with superior accuracy and efficiency while the RSM model can describe the interactions between factors.

Combining Weather Station Data and Short-Term LiDAR Deployment to Estimate Wind Energy Potential with Machine Learning: A Case Study from the Swiss Alps

Wind energy potential in complex terrain is still poorly understood and difficult to quantify. With Switzerland’s current efforts to shift to renewable energy resources, it is now becoming even more crucial to investigate the hidden potential of wind energy. However, the country’s topography makes the assessment very challenging. We present two measurement campaigns at Lukmanier and Les Diablerets, as representative areas of the complex terrain of the Swiss Alps. A general understanding of local wind flow characteristics is achieved by comparing wind speed measurements from a near-surface ultra-sonic anemometer and from light detection and ranging (LiDAR) measurements further aloft. The measurements show how the terrain modifies synoptic wind for example through katabatic flows and effects of local topography. We use an artificial neural network (ANN) to combine the data from the measurement campaign with wind speed measured by weather stations in the surrounding area of the study sites. The ANN approach is validated against a set of LiDAR measurements which were not used for model calibration and also against wind speed measurements from a 25-meter mast, previously installed at Lukmanier. The statistics of the ANN output obtained from multi-year time series of nearby weather stations match accurately the ones of the mast data. However, for the rather short validation periods from the LiDAR, the ANN has difficulties in predicting lowest wind speeds at both sites, and highest wind speeds at Les Diablerets.

GDP responses to supply chain disruptions in a post-pandemic era: Combination of DL and ANN outputs based on Google Trends

With the recent Russian-Ukraine conflict, the frequency and intensity of disruptive shocks on major supply chains have risen, causing increasing food and energy security concerns for regulators. That is, the combination of newly available sophisticated deep learning tools with real-time series data may represent a fruitful policy direction because machines can identify patterns without being pre-conditioned calibration thanks to experimental data training. This paper employs Deep Learning (DL) and Artificial Neural Network (ANN) algorithms and aimed predicts GDP responses to supply chain disruptions, energy prices, economic policy uncertainty, and google trend in the US. Sampled data from 2008 to 2022 are monthly wrangled and embed different recession episodes connected to the subprime crisis of 2008, the COVID-19 pandemic, the recent invasion of Ukraine by Russia, and the current economic recession in the US. Both DL and ANN outputs empirically (and unanimously) demonstrated how sensitive monthly GDP variations are to dynamic changes in supply chain performances. Findings identify the substantial role of google trends in delivering a consistent fit to predicted GDP values, which has implications While a comparative discussion over the larger forecasting performance of DL compared to ANN experiments is offered, implications for global policy, decision-makers and firm managers are finally provided.

New neural network-based algorithm for predicting fatigue life of aluminum alloys in terms of machining parameters

Various classification of aluminum alloys is one of the most common applied materials in transportation industries due to the specific mechanical and material properties. Body of vehicles, including cars, ships, and airplanes, are constantly exposed to different cyclic loads that lead to surface damage and finally, fatigue failure occurs by crack growth. Therefore, the quality of the machined surface has a direct effect on the fatigue life of the products. In this regard, the most important source of surface roughness is the choice of machining parameters. To understand it, turning operations were performed on 2xxx and 7xxx series aluminum alloys considering different values of process parameters. For any series of aluminums, 189 high-cycle fatigue testing specimens were prepared, and experiments were performed by axial tension–compression fatigue test machine in 7 levels of stress (all tests were repeated three times and the mean failure cycles were reported as the fatigue life). Next, an Artificial Neural Network (ANN) based on the Back Propagation (BP) error algorithm was developed to predict fatigue life of Al alloys which are machined with different conditions. To this end, the parameters considered as input variables to the neural network structure include the Yield Strength (YS) and Ultimate Tensile Strength (UTS) to identify the aluminum series, as well as the process parameters such as cutting depth (d), rotational speed (R), and feed speed (V). In addition, the applied cyclic stress (S) to specimen was considered as input. Furthermore, surface roughness (Z) and number of cycles to failure (N) were considered as ANN output. The comparison of the obtained results from ANN predicting with the experimental values indicates that this approach was tuned finely. Eventually, the presented model can be a suitable alternative to perform fatigue tests with high costs and time-consuming.‌‌ Also, the most effective machining parameter on the surface roughness and fatigue limit was reported.

Artificial Intelligence Can Guide Antibiotic Choice in Recurrent UTIs and Become an Important Aid to Improve Antimicrobial Stewardship.

A correct approach to recurrent urinary tract infections (rUTIs) is an important pillar of antimicrobial stewardship. We aim to define an Artificial Neural Network (ANN) for predicting the clinical efficacy of the empiric antimicrobial treatment in women with rUTIs. We extracted clinical and microbiological data from 1043 women. We trained an ANN on 725 patients and validated it on 318. The ANN showed a sensitivity of 87.8% and specificity of 97.3% in predicting the clinical efficacy of empirical therapy. The previous use of fluoroquinolones (HR = 4.23; p = 0.008) and cephalosporins (HR = 2.81; p = 0.003) as well as the presence of Escherichia coli with resistance against cotrimoxazole (HR = 3.54; p = 0.001) have been identified as the most important variables affecting the ANN output decision predicting the fluoroquinolones-based therapy failure. A previous isolation of Escherichia coli with resistance against fosfomycin (HR = 2.67; p = 0.001) and amoxicillin-clavulanic acid (HR = 1.94; p = 0.001) seems to be the most influential variable affecting the output decision predicting the cephalosporins- and cotrimoxazole-based therapy failure. The previously mentioned Escherichia coli with resistance against cotrimoxazole (HR = 2.35; p < 0.001) and amoxicillin-clavulanic acid (HR = 3.41; p = 0.007) seems to be the most influential variable affecting the output decision predicting the fosfomycin-based therapy failure. ANNs seem to be an interesting tool to guide the antimicrobial choice in the management of rUTIs at the point of care.

Minerals resource rent responses to economic performance, greener energy, and environmental policy in China: Combination of ML and ANN outputs

The relationship of mineral resources rent with economic performance, greener energy, and environmental policy is crucial because it could impact how welfare and eco-friendly are treated in a nation. This paper employed machine learning (ML) and Artificial Neural Networks (ANN) to predict minerals resourcesthrougheconomic growth, the producer price index, market prices, the environmental policy stringency index (EPSI), and greener energy in China. The ML and ANN outputs showed how responsive quarterly minerals resource rent variations are too dynamic shifts in economic performance and renewable energy use. The more critical prediction accuracy of ML experiments compared to ANN experiments is highlighted by data from mean absolute percentage, mean square, root mean square, and root mean absolute errors, as well as the coefficient of determination. Though China is abundant in natural resources, there is a need to employ these resources efficiently to achieve long-term eco-friendly performance. In addition to comparing the results of ML and ANN, it also provided causality conclusions and policy implications.

Application of artificial neural networks in performance prediction of cement mortars with various mineral additives

The machine learning technique for prediction and optimization of building material performances became an essential feature in the contemporary civil engineering. The Artificial Neural Network (ANN) prognosis of mortar behavior was conducted in this study. The model appraised the design and characteristics of seventeen either building or high-temperature mortars. Seven different cement types were employed. Seventeen mineral additives of primary and secondary origin were embedded in the mortar mixtures. Cluster Analysis and Principal Component Analysis designated groups of similar mortars assigning them a specific purpose based on monitored characteristics. ANN foresaw the quality of designed mortars. The impact of implemented raw materials on the mortar quality was assessed and evaluated. ANN outputs highlighted the high suitability level of anticipation, i.e., 0.999 during the training period, which is regarded appropriate enough to correctly predict the observed outputs in a wide range of processing parameters. Due to the high predictive accuracy, ANN can replace or be used in combination with standard destructive tests thereby saving the construction industry time, resources, and capital. Good performances of altered cement mortars are positive sign for widening of economical mineral additives application in building materials and making progress towards achieved carbon neutrality by reducing its emission.

The Experimental Study of Dust Effect on Solar Panel Efficiency

This study shows the dust effect on solar panel efficiency based on experimental measurements. Sieve analysis has been performed to investigate the effect on the efficiency of the panel. Dust particles have been divided into different sizes such as (-75), (+75/-105), (+105/-250), (+250/-450). In addition, each piece weighed 20 g, uniformly sprinkled onto a 40-Watt SUNNY polycrystalline panel. In order to obtain the output voltages of the PV panel used a data acquisition card. Additionally, a photodiode sensor has been used in order to obtain the light reflection data from the panel to the outside while there had covered different diameters of dust particles on the panel. The data set consisting of electrical parameters have been used to compered both experimental study and Artificial Neural Network (ANN) output. There were 5 different data sets with the clean PV panel in this study (randomly selected data from it used as %40 training - %60 test) and the clean panel data have been used twice in the training and test part of the ANN. In this study, it has been observed that the ANN method can be used to estimate panel efficiency due to the linearity (The R-value was gotten nearly equal to 1 and it shows that there is a linear relationship between outputs and targets.) if the appropriate transfer function is selected. Also, this study shows that as the particle diameter covering the panel gets smaller, the output voltage of the panel decreases linearly.

A Synthesis-Analysis Machine With Self-Inspection Mechanism for Automatic Design of On-Chip Inductors Based on Artificial Neural Networks

An automatic inductor design process is helpful to reduce the design cycle of radio frequency (RF) integrated circuit (IC). This paper proposed an efficient synthesis-analysis machine (SAM) for on-chip inductor synthesis and modeling, as well as an automatic dataset generation (ADG) topology for the generation of artificial neural networks (ANNs) training dataset. The SAM consists of a synthesis ANN, two analysis ANNs and a proposed self-inspection machine (SIM). For a given design request, the SAM synthesizes a layout first, followed by analyzing the performance of the layout automatically, and finally improves the layout confidence through self-inspection. Compared to the modeling functions of analysis ANN, the synthesis ANN behaves as an inverse model, of which the inputs are desired inductor performances while the outputs are geometrical parameters of the layout. However, multi-value problems might occur in obtaining geometries of inductors, suggesting with evidence from mathematic relationships between geometrical and electrical parameters of an inductor. A multi-valued training dataset will mislead the synthesis ANN, resulting in unqualified layouts. To deal with this issue, a solution space contraction technique (SSCM) is also proposed. Furthermore, the SIM suspends most of the failures by comparing the output of analysis ANN back to the input of synthesis ANN. An electromagnetic simulation tool is used for experiments, and the effectiveness of the proposed SAM is proven by 6,500 inductor samples.

Sensitivity Analysis of Artificial Neural Networks Identifying JWH Synthetic Cannabinoids Built with Alternative Training Strategies and Methods

This paper presents the alternative training strategies we tested for an Artificial Neural Network (ANN) designed to detect JWH synthetic cannabinoids. In order to increase the model performance in terms of output sensitivity, we used the Neural Designer data science and machine learning platform combined with the programming language Python. We performed a comparative analysis of several optimization algorithms, error parameters and regularization methods. Finally, we performed a new goodness-of-fit analysis between the testing samples in the data set and the corresponding ANN outputs in order to investigate their sensitivity. The effectiveness of the new methods combined with the optimization algorithms is discussed.

Artificial Neural Network Technique for Solving Variable Order Fractional Integro-Differential Algebraic Equations

In this paper, we will use an artificial neural network (ANN) to solve the variable order fractional integro-differential algebraic equations (VFIDAEs), which is a three-layer feed-forward neural architecture that is formed and trained using a back propagation unsupervised learning algorithm based on the gradient descent rule for minimizing the error function and parameter modification (weights and biases).When we combine the initial conditions with the ANN output, we get a good approximation of the VFIDAE solution. Finally, the analysis is complemented by two numerical examples that demonstrate the method capability. The collected results show that the suggested strategy is quite successful, resulting in superior approximations in these cases.

Experimental Evaluation and Neural Network Prediction of Fatigue Life of E-Glass Fiber Reinforced Hybrid Composite Material

The present study focuses on investigating the impact of hybrid composite content and loading parameters on the fatigue life of E-glass fiber-reinforced composite. A mathematical model of the fatigue life was developed based on experimentation and expressed as a function of the composite content (Epoxy content (80%, 60%) wt., Novolac content (20%, 40%) wt., E-Fiber Glass content (10%, 20%, and 30%) wt.), and different loads using Adaptive Neural Fuzzy Inference Systems (ANFIS). In order to validate the ANFIS model, 30 fatigue cycle experiments were conducted. Using the experimental data sets, the ANFIS was trained with a momentum algorithm and an average absolute percentage error of 6.045% was obtained and compared to the output of the conventional Artificial Neural Network (ANN) Model. The testing accuracy was then verified with 6 extra experimental data sets and the average predicting error was 5.18%. the predicted ANFIS output data were then used to discuss the effect of the composite content and loading parameters on the fatigue life. The number and shapes of cracks observed on the failed specimens were analyzed under an optical microscope. The analysis revealed that the loading impact showed the highest influence on fatigue life for all types of enforcement and the sequence of strength was E- glass fiber, epoxy, and Novolac contents, respectively.

Machine Learning in Operating of Low Voltage Future Grid

The article is a continuation of the authors’ ongoing research related to power flow and voltage control in LV grids. It outlines how the Distribution System Operator (DSO) can use Machine Learning (ML) technology in a future grid. Based on supervised learning, a Selectively Coherent Model of Converter System Control for an LV grid (SCM_CSC) is proposed. This represents a fresh, new approach to combining off and on-line computing for DSOs, in line with the decarbonisation process. The main kernel of the model is a neural network developed from the initial prediction results generated by regression analysis. For selected PV system operation scenarios, the LV grid of the future dynamically controls the power flow using AC/DC converter circuits for Battery Energy Storage Systems (BESS). The objective function is to maintain the required voltage conditions for high PV generation in an LV grid line area and to minimise power flows to the MV grid. Based on the training and validation data prepared for artificial neural networks (ANN), a Mean Absolute Percentage Error (MAPE) of 0.15% BESS and 0.51–0.55% BESS 1 and BESS 2 were achieved, which represents a prediction error level of 170–300 VA in the specification of the BESS power control. The results are presented for the dynamic control of BESS 1 and BESS 2 using an ANN output and closed-loop PID control including a 2nd order filter. The research work represents a further step in the digital transformation of the energy sector.

Artificial neural network modeling to examine spring turbulators influence on parabolic solar collector effectiveness with hybrid nanofluids

Numerical simulation and artificial neural network modeling of turbulent flow inside a pipe equipped with two spring turbulator samples with two different scales and a segmental cross-section have been investigated. Increased heat transfer rate (HTR) due to the use of a spring turbulator is predicted for the TiO2Cu-Water hybrid nanofluid based on the single-phase model, feed-forward artificial neural network (ANN) and fitting method. The role of Reynolds number (Re), scale and volume fraction (ϕ) on Nusselt number (Nu), pressure drop (ΔP), performance evaluation coefficient (PEC), solar collector efficiency (η), and the field synergy principle (FSP), compared to simple pipe, is considered using the finite volume method. The results show that increasing the spring turbulator scale increased the contact surface of the working fluid and the spring turbulator. As a result, the flow turbulence is increased, which leads to better mixing of the nanofluid as the operating fluid of the solar collector absorber pipe. Finally, ANN outputs and fitting results are compared, and it has been observed that the obtained ANN could predict the targets accurately.

Neural Network With Fourier Series-Based Transfer Functions for Filter Modeling

The Fourier series is introduced as a transfer function (TF) in the artificial neural network (ANN) for parametric modeling of microwave filters in this letter. The reported pole-residue-based TF leads to an order-changing problem of input samples from vector fitting, which is usually solved with an order-tracking technique or data classification. The proposed Fourier series-based TF does not have to carry out the time-consuming operation because the only coefficient order can be determined for all input samples in an iterative process. Compared with the pole-residue-based TF, moreover, the ANN training involves a small number of TF coefficients in the proposed method. The predicted electromagnetic (EM) response is obtained from the coefficients of the ANN output. An example of the ultrawideband (UWB) filter is employed to verify the effectiveness of the Fourier series-based TF.