Proposed Artificial Neural Network Model Research Articles

Design of a Microwave Quadrature Hybrid Coupler with Harmonic Suppression Using Artificial Neural Networks

In this paper, a compact and simple structure of an elliptic microstrip lowpass filter (LPF) is designed for harmonic suppression in microwave quadrature hybrid coupler (QHC) applications. A radial resonator and a rectangular resonator are used to produce an elliptic LPF. The proposed LPF is used on the outer sides of the branch line coupler, which has improved the coupler harmonic suppression. Furthermore, artificial neural networks (ANNs) are incorporated to improve the LPF design process. The LPF best structure is obtained using the proposed ANN model. The proposed LPF has a compact size, which only occupies 16.4 mm × 7.3 mm equals to 0.164 λg × 0.073 λg, has a cut frequency of 2.2 GHz, and shows a sharp transmission band with a roll-off rate of 158.3 dB/GHz. Finally, the deigned QHC operates correctly at 1 GHz, which shows high harmonic suppression ability. The proposed QHC provides wide suppression band from 2.25 GHz up to more than 14 GHz, which can effectively suppress 3rd, to 14th harmonics. The proposed coupler features desirable parameters of S11, S21, S31, and S41, with magnitude of −21 dB, −3.4 dB, −3.3 dB, and −22.5 dB, at the operating frequency. The proposed approach mitigates the complexity of the circuit fabrication, compared with the previous methods while achieved desirable performances for the proposed QHC.

12-Lead ECG Reconstruction Based on Data From the First Limb Lead.

Electrocardiogram (ECG) data obtained from 12 leads are the most common and informative source for analyzing the cardiovascular system's (CVS) condition in medical practice. However, the large number of electrodes, specific placements on the body, and the need for specialized equipment make the ECG acquisition procedure complex and cumbersome. This raises the challenge of reducing the number of ECG leads by reconstructing missing leads based on available data. Most existing methods for reconstructing missing ECG leads rely on utilizing signals simultaneously from multiple known leads. This study proposes a method for reconstructing ECG data in 12 leads using signal data from the first lead, lead I. Such an approach can significantly simplify the ECG registration procedure. The study demonstrates the effectiveness of using unique models with a developed architecture of artificial neural networks (ANNs) to generate the reconstructed ECG signals. Fragments of ECG from lead I, with a duration of 128 samples and a sampling frequency of 100 Hz, are input to the models. ECG fragments can be extracted from the original signal at arbitrary time points. Each model generates an ECG signal of the same length at its output for the corresponding lead. Despite existing limitations, the proposed method surpasses known solutions regarding ECG generation quality when using a single lead. The study shows that introducing an additional feature of the direction of the electrical axis of the heart (EAH) as input to the ANN models enhances the generation quality. The quality of ECG generation by the proposed ANN models is found to be dependent on the presence of CVS diseases. The developed ECG reconstruction method holds significant potential for use in portable registration devices, screening procedures, and providing support for medical decision-making by healthcare specialists.

Predictive and experimental analysis of forces in die-less forming using artificial intelligence techniques

The use of a novel technology for producing the components of lightweight materials and to reduce the requirements of power utilized during manufacturing processes can be a great aspect to decrease pollution and save resources. Single point incremental forming (SPIF) is the viable and novel approach for manufacturing the parts of high strength and lightweight materials without involving dedicated tools and dies economically. This die-less forming technique outperforms the conventional forming techniques by saving the energy and materials. In this work, the estimation and investigation of forming forces have been accomplished to ensure the secure uses for the SPIF machines for performing this process for the designed conditions on AA2024 sheets which is a lightweight aluminum alloy being widely used in aerospace and automotive sectors. To predict the peak deforming load, machine learning (ML) techniques are employed in the current work along with the artificial neural network (ANN) by taking experimental results as the input dataset. The proposed ML model revealed better accuracy (99%) than previous work performed using similar approaches. The proposed ANN model produced lower mean absolute percentage error 4.35 as compared to other models. Authors also calculated the computing time taken during estimation of forming force. Combination of the Flatend-R1 tool and the 1.6 mm blank thickness increased the deforming loads drastically and can become the limiting factor for forming machine which should be avoided whereas the combination of hemispherical tool and lower blank thickness (0.5 mm) reduced the deforming loads that are needed to manufacture the conical frustum. It was also noticed that as the tool shape was changed from hemispherical-end to Flatend-R1, the axial peak forces were increased by 13.16%, 16.59%, 20.43%, and 22.78% for the levels 1, 2, 3, and 4 of the blank thickness, respectively.

Prediction of the Bond Strength of Externally Bonded FRP Sheets Applied to Concrete via Grooves Technique Using Artificial Neural Networks

The present study aims to fill a gap in the literature on the estimation of the bond strength of fiber reinforced polymer sheets bonded to concrete, via the externally bonded reinforcement on grooves (EBROG) technique, employing the curve-fitting on existing datasets in the literature and the methodology of Artificial Neural Networks (ANNs). Therefore, a dataset of 39 experimental results derived from EBROG technique is collected from the literature. A mathematical equation for the bond strength of FRP sheets applied on concrete via the EBROG technique was suggested using curve-fitting and general regression. The proposed mathematical equation is compared and validated with experimental results. The developed ANN model was constructed after testing diverse hidden layers and neurons to find the optimal predictions. The validation of the model is carried out using the experimental results and a statistical analysis is applied to assess the proposed mathematical equation and the proposed ANN model. Furthermore, a parametric study using the ANN model was also performed to investigate the influence of various factors on the bond strength of FRP sheets bonded to concrete. The parametric study proves that the bond strength increases with increasing the tensile stiffness per width, the FRP sheet width, and the concrete compressive strength; however, the effect of the Groove’s width and depth is found to be not monotonous.

Artificial neural network models for forecasting the combustion and emission characteristics of ethanol/gasoline DFSI engines with combined injection strategy

Ethanol was a viable alternative and renewable fuel for spark ignition engines, and ethanol/gasoline dual-fuel combustion with combined injection strategy can strengthen the benefits of using ethanol in engines. However, delicate calibration and optimization were required for ethanol/gasoline DFSI engines due to their higher control complexity and flexibility. Therefore, Artificial Neural Network (ANN) models were developed to represent the performance of DFSI engines. This study established the architecture for the ANN models of ethanol/gasoline DFSI engines, and the topology of 8-36-24-3, 8-64-70-6, 8-48-48-3, and 8-36-16-2 were suggested for the modeling of performance, combustion characteristics, gaseous and PN emissions, respectively. The non-linear relationship between the core control variables and performance, combustion, and emission characteristics of ethanol/gasoline engines can be accurately mapped by the proposed ANN models. The regression values were within the range of 0.9387–0.9962, and the mean square relative errors were within the range of 0.000184–0.03935 between the ANN predicted and experimentally measured results. Moreover, the ANN models had the advantages of high accuracy, sound model completeness, superior robustness, and satisfied reliability, which were desirable for the calibration and optimization of engines.

Application of machine learning technique to predict the energy performance of energy tunnels

This study investigates the application of a machine learning technique in the field of energy tunnels (i.e. tunnels equipped as ground heat exchangers), as a method to efficiently predict and optimise the thermal exchange during the operational phase and reduce the related computational time and resources. Artificial Neural Networks (ANN) models were trained based on short-term (i.e. some years) numerical simulation data. Then, the accuracy of the trained model was assessed by comparing its results for longer operational periods under the same or different thermal operation patterns with respect to those used for training. Numerical simulations were run for long-term periods, and the obtained numerical results were assumed to be the target to verify the accuracy of the prediction. The analyses under different scenarios indicate that the trained models are able to accurately and rapidly predict the thermal exchange of energy tunnels, not only for future years under the same operational pattern, but also for other thermal operation patterns. In this study, using the same computational machine, a 20-year numerical forecast of thermal output takes hours or days depending on complexity of the numerical model, but it can be reduced to minutes by using the proposed ANN model. This represents a first attempt demonstrating that with further development, machine learning techniques could be used in the practice, in combination with short-term monitoring data, as a tool for the assessment of long-term energy potential and optimisation of energy tunnels during operation.

Characteristic and Allowable Compressive Strengths of Dendrocalamus Sericeus Bamboo Culms with/without Node Using Artificial Neural Networks

The strength of construction material is a crucial consideration in the process of structural design and construction. Conventional materials such as concrete or steel have been widely utilized due to their predictable material performance. However, a significant obstacle to the widespread use of bamboo in structural elements lies in the challenge of its standardization. Many previous research studies have explored bamboo’s load bearing capacity, but the information remains limited due to variations in species, size, age, physical properties, moisture content, and other factors, making it difficult to predict their load-bearing capacity. This study aims to propose Artificial Neural Network (ANN) models to predict ultimate compressive load and compressive strength of Dendrocalamus Sericeus bamboo culm. Additionally, for structural design purposes, the proposed ANN models were employed to determine the characteristic and allowable compressive strengths. As a first step, experimental data from compressive tests in the literature were used for training and developing the ANN model. To investigate the effect of the node on compressive loading capacities, the test data were separated into two datasets, “Node” samples and “Internode” samples. Through the training process, ANN models were finally proposed, and the R-square values for the prediction of ultimate compressive load and compressive strength from the proposed ANN models were significantly higher than those obtained from the linear regression analyses used in the literature. Subsequently, the characteristic and allowable compressive strengths were calculated and compared to the strengths obtained from the experiment data, revealing a difference of approximately only 8.0%. Overall, the ANN models presented in this study offer promising predictive ability for both ultimate compressive load and compressive strength of Dendrocalamus Sericeus bamboo culm, as well as for determining characteristic and allowable strengths. Hence, ANN models are suggested to be adopted as a tool for the design and construction of bamboo buildings.

Obtaining an accurate prediction model for viscosity of a new nano-lubricant containing multi-walled carbon nanotube-titanium dioxide nanoparticles with oil SAE50

This study aims to investigate the viscosity behavior of multi-walled carbon nanotube (MWCNT) - titanium dioxide (TiO2) (40−60) - SAE50 oil nanofluid using an Artificial Neural Network (ANN) modeling approach. The main objective is to develop a highly accurate predictive model for viscosity by considering three input parameters: temperature, solid volume fraction (SVF), and shear rate (SR). Rheological measurements provide experimental data used to train and validate the ANN model. The ANN model's architecture, activation functions, and training algorithms are carefully chosen. Data are divided to three subsets including train, validation and test. ANN is trained using trainlm algorithm for 50 times to vanish the effect of random nature of ANN weight initialization. The trained ANN model is then utilized to predict the viscosity of the nanofluid under varying conditions. The results demonstrate the efficacy of the proposed ANN model in capturing the complex relationship between viscosity and the input parameters, providing accurate viscosity predictions for the MWCNT-TiO2-oil SAE50 nanofluid. Furthermore, the influence of temperature, SVF, and SR on viscosity is analyzed, offering valuable insights into the flow behavior of the nanofluid. According to the obtained results, the developed ANN model presents a reliable and efficient approach to estimate the viscosity of the MWCNT-TiO2-SAE50 oil nanofluid, eliminating the need for costly and extensive experimental measurements within the analyzed range. ANN could model the nanofluid viscosity with R2 = 0.9998 and MSE= 0.000189 that is quite acceptable. Also, the experimental data revealed that for the investigated nanofluid, temperature and shear rate have impressive effect on the viscosity (changing viscosity more than 100% for the analyzed margin), on the other hand, the nanoparticle volume fraction effect is much lower, to be more precise, increasing the nanoparticle percentage will increase the viscosity mean value around 30%.

Strain rate sensitivity of strain‐hardening fiber‐reinforced concrete subjected to dynamic direct tensile loading

AbstractThis study investigates the effects of matrix strength on the high‐rate tensile resistance and strain rate sensitivity of strain‐hardening fiber‐reinforced concretes (SHFRCs) under direct tensile loading. Two types of deformed steel fiber, hooked and twisted, were reinforced in two matrices, high‐performance concrete (HPC) with compressive strength of 84 MPa and ultra‐high‐performance concrete (UHPC) with compressive strength of 180 MPa. Additionally, an artificial neural network (ANN) based model was proposed to estimate the strain rate sensitivity of SHFRCs subjected to dynamic direct tensile loading. The results showed that HPC produced relatively lower post cracking strength but much higher strain capacity at a high strain rate than UHPC. Moreover, HPC was found to be more strain rate sensitivity than UHPC. Furthermore, the proposed ANN model was an efficient tool for predicting the strain rate sensitivity of SHFRCs. Based on the sensitivity analysis, the contribution of each influence factor on the final strain rate sensitivity of SHFRCs can be determined.

Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context

This study reports on the successful use of a machine learning approach using attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy for the classification and prediction of a donor’s sex from the fingernails of 63 individuals. A significant advantage of ATR FT-IR is its ability to provide a specific spectral signature for different samples based on their biochemical composition. The infrared spectrum reveals unique vibrational features of a sample based on the different absorption frequencies of the individual functional groups. This technique is fast, simple, non-destructive, and requires only small quantities of measured material with minimal-to-no sample preparation. However, advanced multivariate techniques are needed to elucidate multiplex spectral information and the small differences caused by donor characteristics. We developed an analytical method using ATR FT-IR spectroscopy advanced with machine learning (ML) based on 63 donors’ fingernails (37 males, 26 females). The PLS-DA and ANN models were established, and their generalization abilities were compared. Here, the PLS scores from the PLS-DA model were used for an artificial neural network (ANN) to create a classification model. The proposed ANN model showed a greater potential for predictions, and it was validated against an independent dataset, which resulted in 92% correctly classified spectra. The results of the study are quite impressive, with 100% accuracy achieved in correctly classifying donors as either male or female at the donor level. Here, we underscore the potential of ML algorithms to leverage the selectivity of ATR FT-IR spectroscopy and produce predictions along with information about the level of certainty in a scientifically defensible manner. This proof-of-concept study demonstrates the value of ATR FT-IR spectroscopy as a forensic tool to discriminate between male and female donors, which is significant for forensic applications.

Deep learning based porosity prediction for additively manufactured laser powder-bed fusion parts

Machine learning techniques are extensively used to understand and predict complex non-linear phenomena across various applications. Moreover, these techniques minimize the time and costs associated with experimental and numerical analysis. In this work, a deep learning technique, specifically artificial neural networks (ANN), was employed to predict the density/porosity of laser powder-bed fusion (LPBF) additively manufactured (AM) parts by training the ANN model with X-ray computed tomography (CT) images. In addition to the experimental data, synthetic CT data was generated and used to improve the performance of the ANN model. The ANN model was then optimized for the number of hidden layers and neurons. Different errors like mean absolute error (MAE), root mean square error (RMSE), and square of co-relation coefficient (R2) were used as performance metrics to determine the accuracy and effectiveness of the network. The proposed ANN model was validated and showed excellent predictions (R2 = 0.9981, MAE = 1.6944 x 10−5). The framework proposed in this work can be used to speed-up the quality assurance of AM parts by reducing the time required for the analysis of CT data.

A comparative study to estimate the mode I fracture toughness of rocks using several soft computing techniques

Fracture toughness is an important phenomenon to reveal the actual strength of fractured rock materials. It is, therefore, crucial to use the fracture toughness models principally for simulating the performance of fractured rock medium. In this study, the mode-I fracture toughness (KIC) was investigated using several soft computing techniques. For this purpose, an extensive literature survey was carried out to obtain a comprehensive database that includes simple and widely used mechanical rock parameters such as uniaxial compressive strength (UCS) and Brazilian tensile strength (BTS). Several soft computing techniques such as artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), gene expression programming (GEP), and multivariate adaptive regression spline (MARS) were attempted to reveal the availability of these methods to estimate the KIC. Among these techniques, it was determined that ANN presents the best prediction capability. The correlation of determination value (R2) for the proposed ANN model is 0.90, showing its relative success. In this manner, the present study can be declared a case study, indicating the applicability of several soft computing techniques for the evaluation of KIC. However, the number of samples for different rock types should be increased to improve the established predictive models in future studies.

Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability.

Introduction: Predicting ventricular arrhythmia Torsade de Pointes (TdP) caused by drug-induced cardiotoxicity is essential in drug development. Several studies used single biomarkers such as qNet and Repolarization Abnormality (RA) in a single cardiac cell model to evaluate TdP risk. However, a single biomarker may not encompass the full range of factors contributing to TdP risk, leading to divergent TdP risk prediction outcomes, mainly when evaluated using unseen data. We addressed this issue by utilizing multi-in silico features from a population of human ventricular cell models that could capture a representation of the underlying mechanisms contributing to TdP risk to provide a more reliable assessment of drug-induced cardiotoxicity. Method: We generated a virtual population of human ventricular cell models using a modified O'Hara-Rudy model, allowing inter-individual variation. and Hill coefficients from 67 drugs were used as input to simulate drug effects on cardiac cells. Fourteen features (, , , , , , , , , , , , qNet, qInward) could be generated from the simulation and used as input to several machine learning models, including k-nearest neighbor (KNN), Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN). Optimization of the machine learning model was performed using a grid search to select the best parameter of the proposed model. We applied five-fold cross-validation while training the model with 42 drugs and evaluated the model's performance with test data from 25 drugs. Result: The proposed ANN model showed the highest performance in predicting the TdP risk of drugs by providing an accuracy of 0.923 (0.908-0.937), sensitivity of 0.926 (0.909-0.942), specificity of 0.921 (0.906-0.935), and AUC score of 0.964 (0.954-0.975). Discussion and conclusion: According to the performance results, combining the electrophysiological model including inter-individual variation and optimization of machine learning showed good generalization ability when evaluated using the unseen dataset and produced a reliable drug-induced TdP risk prediction system.

Sensing Spontaneous Combustion in Agricultural Storage Using IoT and ML

The combustion of agricultural storage represents a big hazard to the safety and quality preservation of crops during lengthy storage times. Cotton storage is considered more prone to combustion for many reasons, i.e., heat by microbial growth, exothermic and endothermic reactions in storage areas, and extreme weather conditions in storage areas. Combustion not only increases the chances of a big fire outbreak in the long run, but it may also affect cotton’s quality factors like its color, staple length, seed quality, etc. The cotton’s quality attributes may divert from their normal range in the presence of combustion. It is difficult to detect, monitor, and control combustion. The Internet of Things (IoT) offers efficient and reliable solutions for numerous research problems in agriculture, healthcare, business analytics, and industrial manufacturing. In the agricultural domain, the IoT provides various applications for crop monitoring, warehouse protection, the prevention of crop diseases, and crop yield maximization. We also used the IoT for the smart and real-time sensing of spontaneous combustion inside storage areas in order to maintain cotton quality during lengthy storage. In the current research, we investigate spontaneous combustion inside storage and identify the primary reasons for it. Then, we proposed an efficient IoT and machine learning (ML)-based solution for the early sensing of combustion in storage in order to maintain cotton quality during long storage times. The proposed system provides real-time sensing of combustion-causing factors with the help of the IoT-based circuit and prediction of combustion using an efficient artificial neural network (ANN) model. The proposed smart sensing of combustion is verified by a different set of experiments. The proposed ANN model showed a 99.8% accuracy rate with 95–98% correctness and 97–99% completeness. The proposed solution is very efficient in detecting combustion and enables storage owners to become aware of combustion hazards in a timely manner; hence, they can improve the storage conditions for the preservation of cotton quality in the long run. The whole article consists of five sections.

Dynamic viscosity prediction using artificial intelligence for an antifreeze containing MWCNT–alumina hybrid nanopowders

This paper investigates the impact of Solid Volume Fraction (SVF) and temperature on the dynamic viscosity of a hybrid antifreeze composed of MWCNTs and aluminum oxide in a mixture of water (80%) and ethylene-glycol (20%). An Artificial Neural Network (ANN) is used to predict the viscosity of the nanofluid, which was generated at different SVFs ranging from 0.25% to 1% and temperatures ranging from 25 °C to 50 °C. This study aims to establish a correlation between viscosity and input parameters in the antifreeze. Results demonstrate that Shear Rate (SR) and SVF have opposite effects on the viscosity of the nanofluid. Increasing SVF leads to a strong increase in viscosity deviation and higher mean values of viscosity while increasing SR results in a sharp decline in both the mean value and variation of the viscosity. The temperature has a smaller impact on viscosity variance than SR and SVF. The proposed ANN model with a two-layer network and 13 neurons having nonlinear activation functions in the hidden layer shows an accurate prediction of viscosity versus inputs. The proposed methodology offers an improvement of up to 10 times in predicting viscosity accuracy as compared to GMDH and decision tree techniques. The findings of this study can have important implications for the design of heat exchangers using nanofluids especially in portable devices.

A deep learning approach to predict and optimise energy in fish processing industries

The fish processing sector is experiencing increased pressure to reduce its energy consumption and carbon footprint as a response to (a) an increasingly stringent energy regulatory landscape, (b) rising fuel prices, and (c) the incentives to improve social and environmental performance. In this paper, a standalone forecasting computational platform is developed to optimise energy usage and reduce energy costs. This short-term forecasting model is achieved using an artificial neural network (ANN) to predict power and temperature at thirty-minute intervals in two cold rooms of a fish processing plant. The proposed ANN function is optimised by genetic algorithms (GA) with simulated annealing algorithms (SA) to model the relationships between future temperature and power and the system variables affecting them. To assess the accuracy of the proposed method, extensive experiments were conducted using real-world data sets. The results of the experiments indicate that the proposed ANN model performs with higher accuracy than (a) the long short-term memory (LSTM) as an artificial recurrent neural network (RNN) architecture, (b) peephole-LSTM, and (c) the gated recurrent unit (GRU). This paper finds that using GA & SA algorithms; ANN parameters can be optimised. The RMSE obtained by the ANN compared with the second-ranked method GRU was consequently 16% and 4% less for the predicted temperature and power. The results in one particular site demonstrate energy cost savings in the range of 15%–18% after applying the forecast-optimiser approach. The proposed prediction model is used in a fish processing plant for energy management and is scalable to other sites.

Taguchi and ANN-based optimization method for predicting maximum performance and minimum emission of a VCR diesel engine powered by diesel, biodiesel, and producer gas

Purpose The study aims to determine the the optimal value of output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The performance parameters like brake thermal efficiency (BTE) and brake specific energy consumption (BSEC), whereas CO emission, HC emission, CO2 emission, NOx emission, exhaust gas temperature (EGT) and opacity are the emission parameters measured during the test. Tests are conducted for 2, 6 and 10 kg of load, 16.5 and 17.5 of CR. Design/methodology/approach In this investigation, the first engine was fueled with 100% diesel and 100% Calophyllum inophyllum oil in single-fuel mode. Then Calophyllum inophyllum oil with producer gas was fed to the engine. Calophyllum inophyllum oil offers lower BTE, CO and HC emissions, opacity and higher EGT, BSEC, CO2 emission and NOx emissions compared to diesel fuel in both fuel modes of operation observed. The performance optimization using the Taguchi approach is carried out to determine the optimal input parameters for maximum performance and minimum emissions for the test engine. The optimized value of the input parameters is then fed into the prediction techniques, such as the artificial neural network (ANN). Findings From multiple response optimization, the minimum emissions of 0.58% of CO, 42% of HC, 191 ppm NOx and maximum BTE of 21.56% for 16.5 CR, 10 kg load and dual fuel mode of operation are determined. Based on generated errors, the ANN is also ranked for precision. The proposed ANN model provides better prediction with minimum experimental data sets. The values of the R2 correlation coefficient are 1, 0.95552, 0.94367 and 0.97789 for training, validation, testing and all, respectively. The said biodiesel may be used as a substitute for conventional diesel fuel. Originality/value The blend of Calophyllum inophyllum oil-producer gas is used to run the diesel engine. Performance and emission analysis has been carried out, compared, optimized and validated.

Rare earths leaching from Philippine phosphogypsum using Taguchi method, regression, and artificial neural network analysis

The Philippines produce some 2.1–3.2 million t phosphogypsum (PG) per year. PG can contain elevated concentrations of rare earth elements (REEs). In this work, the leaching efficiency of the REEs from Philippine PG with H2SO4 was for the first time studied. A total of 18 experimental setups (repeated 3 times each) were conducted to optimize the acid concentration (1–10%), leaching temperature (40–80 °C), leaching time (5–120 min), and solid-to-liquid ratio (1:10–1:2) with the overall goal of maximizing the REE leaching efficiency. Applying different optimizations (Taguchi method, regression analysis and artificial neural network (ANN) analysis), a total REEs leaching efficiency of 71% (La 75%, Ce 72%, Nd 71% and Y 63%) was realized. Our results show the importance of the explanatory variables in the order of acid concentration > temperature > time > solid-to-liquid ratio. Based on the regression models, the REE leaching efficiencies are directly related to the linear combination of acid concentration, temperature, and time. Meanwhile, the ANN recognized the relevance of the solid-to-liquid ratio in the leaching process with an overall R of 0.97379. The proposed ANN model can be used to predict REE leaching efficiencies from PG with reasonable accuracy.

APPLICATION OF NEURAL NETWORK IN PREDICTING THE OUTCOME OF CONSTRUCTION DISPUTES

Surat, India The Indian construction sector has become more organized after implementation of the Real Estate (Regulation and Development) Act, 2016. However, occurrence of disagreement between the stakeholders is still a raising question despite the well-established framework of the Real Estate Regulatory Authority (RERA). Project characteristics such as project cost, project type, project scale and anticipated completion time have an impact on causing these disagreements, which ultimately result in a dispute. The disputes might adversely impact the stakeholders associated with them and may lead the project to delay. In order to establish the interrelationship between the project characteristics causing delay due to disputes, an appropriate soft computing technique can be applied. Therefore, this study highlights the application of Artificial Neural Network (ANN) for forecasting the outcome of a dispute. The inputs for the ANN model are project costs, project type, land area acquired and anticipated completion time of disputed projects from RERA. The proposed ANN model of 4-6-1 feed-forward back propagation structure helps to satisfy the need for quick and accurate resolution of disputes with lowering the stress on the judicial system. Moreover, the stakeholders engaged in the dispute can potentially save a considerable amount of money and time if they have an insight of the potential outcome.

A Method for Fault Section Identification of Distribution Networks Based on Validation of Fault Indicators Using Artificial Neural Network

A fault section in Korean distribution networks is generally determined as a section between a switch with a fault indicator (FI) and a switch without an FI. However, the existing method cannot be applied to distribution networks with distributed generations (DGs) due to false FIs that are generated by fault currents flowing from the load side of a fault location. To identify the false FIs and make the existing method applicable, this paper proposes a method to determine the fault section by utilizing an artificial neural network (ANN) model for validating FIs, which is difficult to determine using mathematical equations. The proposed ANN model is built by training the relationship between the measured A, B, C, and N phase fault currents acquired by numerous simulations on a sample distribution system, and guarantees 100% FI validations for the test data. The proposed method can accurately distinguish genuine and false Fis by utilizing the ability of the ANN model, thereby enabling the conventional FI-based method to be applied to DG-connected distribution networks without any changes to the equipment and communication infrastructure. To verify the performance of the proposed method, various case studies considering real fault conditions are conducted under a Korean distribution network using MATLAB.