Search

This paper presents a novel approach to predicting specific monitoring timeframes for Permanent Pacemaker Implantation (PPMI) requirements following a Transcatheter Aortic Valve Replacement (TAVR). The method combines Quantum Ant Colony Optimization (QACO) with the Adaptive Neuro-Fuzzy Inference System (ANFIS) and incorporates expert knowledge. Although this forecast is more precise, it requires a larger number of predictors to achieve this level of accuracy. Our model deploys expert-derived insights to guarantee the clinical relevance and interpretability of the predicted outcomes. Additionally, we employ quantum computing techniques to address this complex and high-dimensional problem. Through extensive assessments, we show that our quantum-enhanced model outperforms traditional methods with notable improvement in evaluation metrics, such as accuracy, precision, recall, and F1 score. Furthermore, with the integration of eXplainable AI (XAI) methods, our solution enhances the transparency and reliability of medical predictive models, hence promoting improved clinical practice decision-making. The findings highlight how quantum computing has the potential to completely transform predictive analytics in the medical field, especially when it comes to improving patient care after TAVR. [ABSTRACT FROM AUTHOR]

New propulsion systems in aircrafts must meet strict regulations and emission limitations. The Flightpath 2050 goals set by the Advisory Council for Aviation Research and Innovation in Europe (ACARE) include reductions of 75%, 90%, and 65% in CO2, NOx, and noise, respectively. These goals are not fully satisfied by marginal improvements in gas turbine technology or aircraft design. A novel control design procedure for the next generation of turbofan engines is proposed in this paper to improve Full Authority Digital Engine Control (FADEC) systems and reduce the emission levels to meet the Flightpath 2050 regulations. Hence, an Adaptive Network–based Fuzzy Inference System (ANFIS), nonlinear autoregressive network with exogenous inputs (NARX) techniques, and the block-structure Hammerstein–Wiener approach are used to develop a model for a turbofan engine. The Min–Max control structure is chosen as the most widely used practical control algorithm for gas turbine aero engines. The objective function is considered to minimize the emission level for the engine in a pre-defined maneuver while keeping the engine performance in different aspects. The Genetic Algorithm (GA) is applied to find the optimized control structure. The results confirm the effectiveness of the proposed approach in emission reduction for the next generation of turbofan engines. [ABSTRACT FROM AUTHOR]

In recent years, intensive care unit (ICU) doctors have paid more attention to delirium. ICU patients have a high risk of delirium. Delirium can lead to serious adverse outcomes, but early diagnosis and prediction of delirium are very difficult and lack effective assessment tools. The causes of delirium are many and complex, and there is no definite prediction model. To solve this problem, this paper proposes a delirium prediction model based on a hybrid cuckoo search algorithm with stochastic gradient descent—the adaptive-network-based fuzzy inference system (SGDCS-ANFIS) approach. Thirty-five relevant indicators of 1072 ICU cases (536 delirium cases and 536 nondelirium cases) were selected to establish a delirium prediction model to judge whether patients tended to experience delirium. The experiments show that the delirium prediction model based on the hybrid SGDCS-ANFIS approach has better performance than traditional classification and prediction machine learning approaches, and the accuracy is improved to 73.02%. It can provide some reference for the prediction of delirium, promote early diagnosis, and provide knowledge for early intervention to improve the prognosis of ICU patients. Adding this delirium prediction model to the ICU protocol will potentially improve the treatment outcome, quality, and cost. Doctors can manage sudden symptoms more calmly, and patients will also benefit. By collecting the real-time data commonly used in electronic medical records of ICUs, the proposed delirium prediction model can be easily applied in hospitals. Delirium can lead to serious adverse outcomes, but early diagnosis and prediction of delirium are very difficult and lack effective assessment tools. We propose a hybrid SGDCS-ANFIS approach to establish delirium prediction model to judge whether ICU patients tend to experience delirium. It can provide some reference for the prediction of delirium, promote early diagnosis, and provide knowledge for early intervention to improve the prognosis of ICU patients. By collecting the real-time data commonly used in electronic medical records of ICUs, the proposed delirium prediction model can be easily applied in hospitals. Fig. Development flow from raw data to the building of the delirium prediction model and model comparison. [ABSTRACT FROM AUTHOR]

This paper presents a new development of an Adaptive Network-based Fuzzy Inference System (ANFIS) for a Hybrid Ground Source Heat Pump (HGSHP). The HGSHP is equipped with a supplementary heat sink composter to process organic solid waste (OSW), utilizing excess hot air from the condensing unit to aerate the compost pile. The Fuzzy Logic Controller (FLC) was developed using data collected by effective sensors installed in the HGSHP system. The main objective is to control the water flow rate with a Variable Speed Drive (VSD) to improve overall system performance. The dataset for ANFIS has been created and trained using MATLAB® software, then implemented on a Raspberry Pi nano-computer with Python coding. This paper compares the performance of ANFIS with two different cases: ANFIS with Triangular Membership Function (TriMF) and ANFIS with Gaussian Membership Function (GaussMF). After implementing ANFIS with TriMF and GaussMF, the average COP during composter operation and system cooling significantly increased. In contrast, the HGSHP system power consumption is sufficiently reduced in both case studies. Moreover, ANFIS also benefits the composting process, as evidenced by the increase in composter operation time, and vice versa for system cooling time. Ultimately, the implementation of ANFIS can improve the HGSHP system performance in both the TriMF and GaussMF cases, with the TriMF case showing a significant improvement in the HGSHP system performance compared to the GaussMF case.

Many degrees of freedom on engine operating parameters limit the optimizing of engine managements for the sake of simultaneously complying with emission regulations and energy economy requirements. Adaptive neuro-fuzzy inference system (ANFIS) is the combination of neural network and fuzzy logic, able to solve nonlinear problems those do not have algorithmic solutions and cannot be modeled mathematically, thus eliminating the limitations of classical approaches. In this study, ANFIS was employed to map the relationships between controlled boundaries and engine performances. A total number of 80 experimental data on dual-fuel diesel engine were selected for training and testing the ANFIS model which has six input variables (diesel fuel injection timing, gasoline premixed ratio, rate of exhaust gas recirculation, indicated mean effective pressure, and the timings of 10% and 50% of total heat release) within a wide validity ranges of engine operating parameters and four outputs of engine emissions and performance. Then, the ANFIS outputs were used to evaluate the objective functions of the optimization process, which was performed with a genetic algorithms (GA) multi-objective optimizing approach. Finally, the Pareto-optimal sets were plotted with minimized NOx as well as soot emissions within the imposed constraints of pressure rise rate and efficiency. This paper studied the feasibility of using ANFIS in combination with GA to optimize the diesel engine settings so that the optimal engine performance and emission behavior would be obtained. The characteristics of the optimal solutions were ultimately explored by sensitivity analysis. [ABSTRACT FROM AUTHOR]

Clear-sky Direct Normal Irradiance (DNI) is the solar power received at ground level per unit of area, at a specific location, under cloud-free conditions. Regarding Concentrating Solar Power (CSP) technologies, such conditions mean that there is no cloud between the Sun and the observer, i.e. the solar field. Since clear sky defines the nominal operating conditions of CSP plants, real-time estimates and forecasts of clear-sky DNI are key information for power plant operators tasked with the management of those plants. So, the present paper focuses first on a new algorithm for the real-time detection of clear-sky situations from DNI measurements. This algorithm makes use of the last-known clear-sky situation and requires the maximum speed at which the atmosphere becomes opaque to be evaluated. The paper also focuses on an efficient approach to the real-time assessment of clear-sky DNI. This approach combines the model developed by Ineichen and Perez with a persistence of atmospheric turbidity, taking advantage of the fact that changes in this quantity are relatively small throughout the day in comparison to changes in DNI, even when the sky is free of clouds. Performance is evaluated via a comparative study, in which empirical models are included, using one-minute data from two sites (Golden, USA, and Perpignan, France). MAE and RMSE are lower than 10 W m - 2 and 21 W m - 2 , respectively. The same approach is capable of providing accurate intraday forecasts of clear-sky DNI. It has proven to be the best compromise between accuracy and complexity (reference is a persistence of DNI) among the considered approaches, including neuro-fuzzy approaches. For a forecast horizon of 5 h,MAE ≃ 30 W m - 2 and RMSE ≃ 37 W m - 2 . [ABSTRACT FROM AUTHOR]

The radiofrequency ablation temperature system is characterised by its time-varying, non-linear, and hysteretic nature. The application of PID controllers to the control of radiofrequency ablation temperature systems has a number of challenges, including overshoot, dependence on high-precision mathematical models, and difficulty in parameter tuning. Therefore, in order to improve the effectiveness of radiofrequency ablation temperature control, an adaptive network-based fuzzy inference system combined with an incremental PID controller was used to optimise the shortcomings of the PID controller in radiofrequency ablation temperature control. At the same time, the learning rate at the time of updating the consequence parameters was set by segmentation to solve the problem of poor control accuracy when the ANFIS-PID controller is implemented based on FPGA fixed-point decimals. Based on FPGA-in-the-loop simulation experiments and ex vivo experiments, the effectiveness of the ANFIS-PID controller in the temperature control of radiofrequency ablation was verified and compared with the PID controller under the same conditions. The experimental results show that the ANFIS-PID controller has a superior performance in terms of tracking capability and stability compared with the PID controller. [ABSTRACT FROM AUTHOR]

This paper attempts to apply an adaptive network-based fuzzy inference system (ANFIS) for analysis of the resonant frequency of a single-layer single-patch microstrip rectangular patch antenna with two equal size slots which are placed on the patch in the form of parallel to resonance edges. The resonant frequency is calculated as the position of the slots is shifted from the right endpoint to the left endpoint on the patch between −4.2 mm ≤ Xslot ≤ 4.2 mm with the steps of 0.1 mm. The designed antenna is proposed for downlink of X band satellite, broadcasting satellite service, fixed-satellite service uplink, satellite (Earth-to-space), radio navigation, mobile-satellite (Earth-to-space), and KU band which can be achieved at the resonant frequencies of 7.2 GHz, 12.2 GHz, 14.6 GHz, 17.5 GHz and 19.3 GHz. Next, High Frequency Electromagnetic Field Simulation software (ANSYS HFSS) results for the prototype microstrip antenna are compared with the values obtained through ANFIS system. It can be concluded that the adaptive network-based fuzzy inference system in such designs can be conveniently used due to fuzzy system’s high approximation capability and much faster convergence rate. The best results for our ANFIS system can be obtained if Gaussian membership is used which leads to the mean absolute error of 1.4653. [ABSTRACT FROM AUTHOR]

Local scour around bridge piers is a complicated physical process and involves highly three-dimensional flows. Thus, the scour depth, which is directly related to the safety of a bridge, cannot be given in the form of the exact relationship of dependent variables via an analytical method. This paper proposes the use of the adaptive neuro-fuzzy inference system (ANFIS) method for predicting the scour depth around a bridge pier. Five variables including mean velocity, flow depth, size of sediment particles, critical velocity for particles' initiation of motion, and pier width were used for the scour depth. For comparison, predictions by the artificial neural network (ANN) model were also provided. Both the ANN model and ANFIS method were trained and validated. The findings indicate that the modeling with dimensional variables yields better predictions than when normalized variables are used. The ANN model was applied to a field-scale dataset. Prediction results indicated that the errors are much larger compared to the case of a laboratory-scale dataset. The MAPE by the ANN model trained with part of the field data was not seriously different from that by the model trained with the laboratory data. However, the application of the ANFIS method improved the predictions significantly, reducing the MAPE to the half of that by the ANN model. Five selected empirical formulas were also applied to the same dataset, and Sheppard and Melville's formula was found to provide the best prediction. However, the MAPEs for the scour depths predicted by empirical formulas are much larger than MAPEs by either the ANN or the ANFIS method. The ANFIS method predicts much better if the range of the training dataset is sufficiently wide to cover the range of the application dataset. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to predicting specific monitoring timeframes for Permanent Pacemaker Implantation (PPMI) requirements following a Transcatheter Aortic Valve Replacement (TAVR). The method combines Quantum Ant Colony Optimization (QACO) with the Adaptive Neuro-Fuzzy Inference System (ANFIS) and incorporates expert knowledge. Although this forecast is more precise, it requires a larger number of predictors to achieve this level of accuracy. Our model deploys expert-derived insights to guarantee the clinical relevance and interpretability of the predicted outcomes. Additionally, we employ quantum computing techniques to address this complex and high-dimensional problem. Through extensive assessments, we show that our quantum-enhanced model outperforms traditional methods with notable improvement in evaluation metrics, such as accuracy, precision, recall, and F1 score. Furthermore, with the integration of eXplainable AI (XAI) methods, our solution enhances the transparency and reliability of medical predictive models, hence promoting improved clinical practice decision-making. The findings highlight how quantum computing has the potential to completely transform predictive analytics in the medical field, especially when it comes to improving patient care after TAVR.

The primary benefit of fuzzy systems theory is to approximate system behavior where analytic functions or numerical relations do not exist. In soil mechanics relationship between the causes and effects can be observed with laboratory tests but is difficult to develop analytical functions or numerical relations between input and output. In geotechnical optimization the most usual constraints represent state variables of structural response for each loading case. The aim of this paper is to define the soft constrain with adaptive network-based fuzzy inference system (ANFIS) in the soil mechanics. The developed soft constrain is than applied in non-linear programming (NLP) to obtain optimal solution. In the case of soil compaction the performance of the proposed optimization algorithm is evaluated. The main aim of soil compaction is to define optimal water content at which soil can be compacted to a densest state that improve their mechanical and physical properties. [ABSTRACT FROM AUTHOR]

Groundwater is one of the most important natural resources in the world and is widely used for irrigation purposes. Groundwater quality is affected by various natural heterogeneities and anthropogenic activities. Consequently, monitoring groundwater quality and assessing its suitability are crucial for sustainable agricultural irrigation. In this study, the suitability of groundwater for irrigation was determined by using sodium adsorption ratio (SAR), residual sodium carbonate (RSC), Kelly index (KI), percentage of sodium (Na%), magnesium ratio (MR), potential salinity (PS) and permeability index (PI). The groundwater samples were collected and analyzed from 37 different sampling stations for this purpose. Along with suitability analysis, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict irrigation water quality parameters. The models were evaluated by comparing the measured values and the predicted values using the statistical criteria [coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE) and Nash–Sutcliffe efficiency (NS)]. In the estimation of all irrigation water quality parameters, the ANN model has performed much higher compared with the ANFIS model. Spatial distribution maps were generated for measured and ANN model-estimated irrigation water quality indices using the IDW interpolation method. Spatial distributions of groundwater quality indices revealed that MR was higher than the allowable limits in most of the study areas and the other quality criteria were within the permissible limits. It has been determined that the interpolation maps obtained as a result of artificial intelligence methods have appropriate sensitivity when compared with the observed maps. Based on the present findings, ANN models could be used as an efficient tool for estimating groundwater quality indices in unsampled sections of the study area and the other regions with similar conditions. [ABSTRACT FROM AUTHOR]

This paper proposes the use of artificial neural networks and adaptive neuro-fuzzy inference systems for estimating the compressive strength of hollow concrete block masonry prisms. Three main influential parameters, namely the prisms’ height-to-thickness ratio and the compressive strengths of hollow concrete blocks and mortars, were used as input to the models. The two models were trained and tested using 102 data sets obtained from the tests conducted by the authors as well as published technical literatures and then verified by comparison with other empirical calculation methods. The results showed that the proposed models have excellent prediction ability with insignificant error rates. [ABSTRACT FROM AUTHOR]

Geopolymer concrete (GPC) has been used as a partial replacement of Portland cement concrete (PCC) in various construction applications. In this paper, two artificial intelligence approaches, namely adaptive neuro fuzzy inference (ANFIS) and artificial neural network (ANN), were used to predict the compressive strength of GPC, where coarse and fine waste steel slag were used as aggregates. The prepared mixtures contained fly ash, sodium hydroxide in solid state, sodium silicate solution, coarse and fine steel slag aggregates as well as water, in which four variables (fly ash, sodium hydroxide, sodium silicate solution, and water) were used as input parameters for modeling. A total number of 210 samples were prepared with target-specified compressive strength at standard age of 28 days of 25, 35, and 45 MPa. Such values were obtained and used as targets for the two AI prediction tools. Evaluation of the model's performance was achieved via criteria such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results showed that both ANN and ANFIS models have strong potential for predicting the compressive strength of GPC but ANFIS (MAE = 1.655 MPa, RMSE = 2.265 MPa, and R2 = 0.879) is better than ANN (MAE = 1.989 MPa, RMSE = 2.423 MPa, and R2 = 0.851). Sensitivity analysis was then carried out, and it was found that reducing one input parameter could only make a small change to the prediction performance. [ABSTRACT FROM AUTHOR]

Determination of the essential properties of petroleum reservoir rocks and parameters of fluid flow in the porous media is a challenging part of reservoir characterization. Among other reservoir rock and two-phase flow properties, the relative permeabilities of gas–oil and water–oil systems are required as critical inputs for reservoir simulation models. Generally, laboratory experiments are considered the best way to obtain accurate information on reservoir rock and flow properties. However, due to the high costs and lengthy procedures of laboratory analyses of reservoir rocks and due to other reasons such as unavailability of the required rock samples, particularly during the early stages of reservoir life, the use of other techniques to predict these properties is inevitable. In this study, three advanced machine learning methods were used to predict relative permeability of oil in the presence of water (krow), relative permeability of water (krw), relative permeability of oil in the presence of gas (krog), and relative permeability of gas (krg). These methods are adaptive network-based fuzzy inference system (ANFIS), the hybrid of least square support vector machine (LSSVM) with coupled simulated annealing (CSA), and extremely randomized trees, also known as extra trees (ET). We used two extensive sets of experimental data on relative permeabilities in the water–oil and gas–oil systems to develop models with good generalization and universality. The prediction accuracy and reliability of the developed models were evaluated using two standard statistical quality measures, i.e., root mean square deviation (RMSD) and the regression coefficient (R2). The results show that while all the developed models were capable of matching the experimental results with reasonable accuracies, the ET and hybrid LSSVM–CSA models provided better prediction performance and superior correlations between the measured and predicted values. Moreover, this study shed light on to what degree each rock-flow property is important in the estimation of water–oil and gas–oil relative permeabilities. In this regard, we applied feature reduction based on the ET feature importance rankings for developing new ET models to estimate water–oil relative permeabilities using only two input features, i.e., Sw and Swc, for prediction of krow, and Sw and wettability type for prediction of krw, instead of the seven input parameters used in development of the main models. [ABSTRACT FROM AUTHOR]

New propulsion systems in aircrafts must meet strict regulations and emission limitations. The Flightpath 2050 goals set by the Advisory Council for Aviation Research and Innovation in Europe (ACARE) include reductions of 75%, 90%, and 65% in CO2, NOx, and noise, respectively. These goals are not fully satisfied by marginal improvements in gas turbine technology or aircraft design. A novel control design procedure for the next generation of turbofan engines is proposed in this paper to improve Full Authority Digital Engine Control (FADEC) systems and reduce the emission levels to meet the Flightpath 2050 regulations. Hence, an Adaptive Network–based Fuzzy Inference System (ANFIS), nonlinear autoregressive network with exogenous inputs (NARX) techniques, and the block-structure Hammerstein–Wiener approach are used to develop a model for a turbofan engine. The Min–Max control structure is chosen as the most widely used practical control algorithm for gas turbine aero engines. The objective function is considered to minimize the emission level for the engine in a pre-defined maneuver while keeping the engine performance in different aspects. The Genetic Algorithm (GA) is applied to find the optimized control structure. The results confirm the effectiveness of the proposed approach in emission reduction for the next generation of turbofan engines.

• New reliable models are developed to obtain bubble point pressure and oil formation volume factor. • The developed models consider vital input parameters such as solution gas-oil ratio and gas specific gravity. • Great agreement between real data and model predictions is noticed. • Temperature has the lowest importance in the models. • ET models need less parameters, compared to existing correlations. Successful field development relies on effective reservoir management, which, in turn, is, to a great extent, influenced by the knowledge of reservoir fluid properties and phase behaviour. Laboratory experiments, known as Pressure-Volume-Temperature (PVT) tests, are the most reliable sources of detailed information on reservoir fluid properties required for different recovery stages and methods. However, these laboratory experiments are costly and time-consuming. Also, the experimental data of reservoir fluids are not readily available in many cases, particularly in early stages of field development when such data are required for estimation of the in-place fluid volumes and/or design of enhanced recovery processes. For these reasons, the development of models for prediction of reservoir fluid properties, especially the bubble point pressure (P b) and oil formation volume factor (B o), has been an active area of research since decades ago. In this paper, we have used the effective machine learning approaches of Extremely Randomized Trees, also called Extra Trees (ET), Least Square Support Vector Machine (LSSVM) hybridized with the Coupled Simulated Annealing (CSA) algorithm, and Adaptive Network-based Fuzzy Inference System (ANFIS) to develop models for prediction of B o and P b. An extensive dataset of the essential reservoir and fluid properties comprised of initial solution gas-oil ratio (R si), gas specific gravity (γ g), oil API gravity (API), and reservoir temperature (T), in addition to the target parameters, has been used to introduce the models. The main focus of this study is on the ET modelling method which we have used as a robust tool to predict the fluid properties of interest with high accuracy over wide ranges of the input features. The other two modelling techniques, i.e. LSSVM-CSA hybrid and ANFIS, have been used in this work to provide a basis for comparison and evaluation of the models. The predictions of the developed models greatly match the real data. Through detailed model evaluation and error analysis, it is shown that the ET model outperforms the other models in terms of accuracy and robustness (with very small mean absolute percentage errors of 0.099% and 0.917% for B o and P b predictions, respectively, over the total dataset), although all the developed models exhibit strong performance and comparably accurate predictions. Moreover, the developed ET models show a strong correlation between the predicted data and the target values with R2 > 0.99 in almost all cases over the training, test, and total datasets. The findings of this study can help to better understand the relative importance of the different reservoir fluid parameters typically used for the prediction of B o and P b. Based on this work, with the ET approach, P b and R si have the highest importance in determining B o and P b , respectively, while temperature has the lowest significance in both cases. To the best of our knowledge, this work is the first B o and P b modelling attempt using the ET method. Additionally, we have applied the feature selection capability of the tree-based estimators to develop a new ET model for prediction of P b using only two parameters (R si and γ g); an outcome that is of particular importance in case of the lack of data on other parameters which are required in almost all the other P b predictive tools and correlations reported in the literature. [ABSTRACT FROM AUTHOR]

Distributed sensors are mainly used environment monitoring systems to monitor volcanic eruption, weather disaster, storm, tornadoes, hurricane, earthquake, and tsunami. The routing path of these networks is frequently affected by the presence of malicious and faulty nodes. The authors propose a secure, load‐balanced routing (SLBR) protocol using wireless sensor networks for disaster management. The proposed protocol consists of a logical clustering technique and an efficient key management system. The improved moth‐flame optimisation algorithm is used to compute the balanced clusters. The leader node is computed by the rank values with a tool of adaptive network‐based fuzzy inference system. Then, the hybrid signcryption algorithm‐based key management system for secure data transmission between the source node and destination node. The experimental result shows that the proposed SLBR protocol improves the security and balances the load compared to existing protocols. [ABSTRACT FROM AUTHOR]

This work focuses on the correlations among water quality parameters such as total hardness, total dissolved solids, electrical conductivity and total alkalinity with water parameters pH, temperature and the sum of mill equivalents of cations and anions in water reservoir in Kermanshah province (located in the middle of the western part of Iran). The data of water quality of monitoring sites were collected over year 2015. To predict and simulate water quality parameters, two data-driven models, i.e., adaptive neural fuzzy inference system and a hybrid adaptive neural fuzzy inference system structure trained by particle swarm optimization technique, were used. The main advantages of these methods are their high accuracy and very fast computational speed to predict unknown data. The results indicated that implementation of two models is highly satisfactory for predicting inorganic indicators of water quality. However, the flexibility of particle swarm optimization–adaptive neural fuzzy inference system method in modeling is better than adaptive neural fuzzy inference system approach. To prove this, the correlation coefficient, mean absolute error, root mean square error and t statistics were calculated as the error criterion. The overall (training and testing) mean relative error percentage, mean absolute error, root mean square error, correlation coefficient and t statistics obtained by the proposed particle swarm optimization–adaptive neural fuzzy inference system model are less than 3.50, 11.60, 18.90, 0.95 and 0.38%, respectively. The results provide a useful approach that uses water parameters to estimate water quality in water reservoir for water treatment and pollution management. [ABSTRACT FROM AUTHOR]

Geopolymer concrete (GPC) is applied successfully in the construction of civil engineering structures. This outcome confirmed that GPC can be used as an alternative material to conventional ordinary Portland cement concrete (OPC). Recent investigations were attempted to incorporate recycled aggregates into GPC to reduce the use of natural materials such as stone and sand. However, traditional methodology used to predict compressive strength and to find out an optimum mix for GPC is yet to be formulated, especially in cases of GPC using by-products as aggregates. In this study, we propose novel hybrid artificial intelligence (AI) approaches, namely a particle swarm optimization (PSO)-based adaptive network-based fuzzy inference system (PSOANFIS) and a genetic algorithm (GA)-based adaptive network-based fuzzy inference system (GAANFIS) to predict the 28-day compressive strength of GPC containing 100% waste slag aggregates. To construct and validate these models, 21 different mixes with 210 specimens were casted and tested. Three input parameters were used to predict the tested compressive strength of GPC, i.e., the sodium solution (NaOH) concentration (varied from 10 to 14 M), the mass ratio of alkaline activation solution to fly ash (AAS/FA), ranging from 0.4 to 0.5, and the mass ratio of sodium silicate (Na2SiO3) to sodium hydroxide solution (SS/SH) which was varied from 2 to 3. The compressive strength of the fabricated GPC was used as output parameter for the prediction models. Validation of the models was done using several statistical criteria such as mean absolute error (MAE), root-mean-square error (RMSE), and correlation coefficient (R). The results show that the PSOANFIS and GAANFIS models have strong potential for predicting the 28-day compressive strength of GPC, but the PSOANFIS (MAE = 1.847 MPa, RMSE = 2.251 MPa, and R = 0.934) was slightly better than the GAANFIS (MAE = 2.115 MPa, RMSE = 2.531 MPa, and R = 0.927). This study will help in reducing the time and cost for the implementation of laboratory experiments in designing the optimum proportions of GPC. [ABSTRACT FROM AUTHOR]

An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system (ANFIS) model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules: the astronomical tide module caused by celestial bodies' movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system (FIS) structure, three approaches which include grid partition (GP), fuzzy c-means (FCM) and sub-clustering (SC) are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability. [ABSTRACT FROM AUTHOR]

Geopolymer concrete (GPC) has been used as a partial replacement of Portland cement concrete (PCC) in various construction applications. In this paper, two artificial intelligence approaches, namely adaptive neuro fuzzy inference (ANFIS) and artificial neural network (ANN), were used to predict the compressive strength of GPC, where coarse and fine waste steel slag were used as aggregates. The prepared mixtures contained fly ash, sodium hydroxide in solid state, sodium silicate solution, coarse and fine steel slag aggregates as well as water, in which four variables (fly ash, sodium hydroxide, sodium silicate solution, and water) were used as input parameters for modeling. A total number of 210 samples were prepared with target-specified compressive strength at standard age of 28 days of 25, 35, and 45 MPa. Such values were obtained and used as targets for the two AI prediction tools. Evaluation of the model’s performance was achieved via criteria such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results showed that both ANN and ANFIS models have strong potential for predicting the compressive strength of GPC but ANFIS (MAE = 1.655 MPa, RMSE = 2.265 MPa, and R2 = 0.879) is better than ANN (MAE = 1.989 MPa, RMSE = 2.423 MPa, and R2 = 0.851). Sensitivity analysis was then carried out, and it was found that reducing one input parameter could only make a small change to the prediction performance.

This paper attempts to apply an adaptive network-based fuzzy inference system (ANFIS) for analysis of the resonant frequency of a single-layer single-patch microstrip rectangular patch antenna with two equal size slots which are placed on the patch in the form of parallel to resonance edges. The resonant frequency is calculated as the position of the slots is shifted from the right endpoint to the left endpoint on the patch between −4.2 mm ≤ Xslot ≤ 4.2 mm with the steps of 0.1 mm. The designed antenna is proposed for downlink of X band satellite, broadcasting satellite service, fixed-satellite service uplink, satellite (Earth-to-space), radio navigation, mobile-satellite (Earth-to-space), and KU band which can be achieved at the resonant frequencies of 7.2 GHz, 12.2 GHz, 14.6 GHz, 17.5 GHz and 19.3 GHz. Next, High Frequency Electromagnetic Field Simulation software (ANSYS HFSS) results for the prototype microstrip antenna are compared with the values obtained through ANFIS system. It can be concluded that the adaptive network-based fuzzy inference system in such designs can be conveniently used due to fuzzy system’s high approximation capability and much faster convergence rate. The best results for our ANFIS system can be obtained if Gaussian membership is used which leads to the mean absolute error of 1.4653.

Despite the widespread use of time series models in stock index forecasts, some of these models have encountered problems: (1) the selection of input factors may depend on personal experience or opinion; and (2) most conventional time series models consider only one variable. Furthermore, traditional forecasting models suffer from the following drawbacks: (1) models may rely on restrictive assumptions (such as linear separability or normality) about the variables being analyzed; and (2) it is hard to define and select applicable input factors for artificial neural networks (ANNs) in particular, and the rules generated from ANNs are not easily understood. To address these issues, we propose a multi-factor time series model based on an adaptive network-based fuzzy inference system (ANFIS) for stock index forecasting. In the proposed model, stepwise regression was first applied for the objective selection of technical indicators and then combined with ANFIS to construct the forecasting model. We evaluated the performance of our proposed model against three other models, with transaction data from the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the Hong Kong Hang Seng Index (HSI) stock markets from 1998 to 2006 as experimental data sets and the root mean square error (RMSE) as the evaluation criterion. The results show the superiority of the proposed combined model, which outperformed other models in terms of RMSE and profitability, with strategies for increasing long-term uses of stock index forecasts made on the TAIEX and the HSI. [ABSTRACT FROM AUTHOR]

One of the most important functions of distribution automation is automated fault location. The knowledge-based techniques are becoming more successful in enhancing the accuracy of fault location in power distribution networks. Adaptive network-based fuzzy inference system (ANFIS) with hybrid learning algorithm was employed in this article to arrive at an accurate fault location. Three main estimations were done by the ANFIS fault models designed and developed in MATLAB® - fault type classification, faulted line-segment detection, and fault location on that faulted line segment. This estimated information is vital for distribution substation engineer, and greatly facilitates the substation maintenance crew in reaching the faulty spot quickly for repair and power restoration. The results obtained demonstrate higher levels of accuracy and efficiency, and thereby can greatly enhance the power system reliability and quality. [ABSTRACT FROM AUTHOR]

This study proposes a new method for modelling of metal‐oxide surge arresters (MOSAs) thermal balance diagram based on power loss and heat dispersion curve. Adaptive network‐based fuzzy inference system (ANFIS)‐based method was offered to compute surge arrester power loss characteristic. Thermal stability (TS) of surge arrester, which is analysed by thermal balance diagram, has a great influence on their proper functions. MOSA material variation is an important factor that has an influence on its TS. Degradation factor (DF) was introduced as a new index in this study to represent material variations of MOSA. Therefore, applied voltage, temperature and DF were considered as inputs in ANFIS model to obtain accurate power loss which is a very important factor in TS characteristic. Heat dispersion curve was obtained as a main feature in the thermal balance diagram from surge arrester cooling curve. Degrading effect was considered using voltage and current measured from MOSA which are degraded by utilisation in the network. In addition, effect of assembling process has been considered by using new different varistors in experimental measurements. This study shows that the thermal balance diagram of surge arrester is predictable with a good accuracy by using artificial model. [ABSTRACT FROM AUTHOR]

This study applies Real-Time Recurrent Learning Neural Network (RTRLNN) and Adaptive Network-based Fuzzy Inference System (ANFIS) with novel heuristic techniques to develop an advanced prediction model of accumulated total inflow of a reservoir in order to solve the difficulties of future long lead-time highly varied uncertainty during typhoon attacks while using a real-time forecast. For promoting the temporal-spatial forecasted precision, the following original specialized heuristic inputs were coupled: observed-predicted inflow increase/decrease (OPIID) rate, total precipitation, and duration from current time to the time of maximum precipitation and direct runoff ending (DRE). This study also investigated the temporal-spatial forecasted error feature to assess the feasibility of the developed models, and analyzed the output sensitivity of both single and combined heuristic inputs to determine whether the heuristic model is susceptible to the impact of future forecasted uncertainty/errors. Validation results showed that the long lead-time--predicted accuracy and stability of the RTRLNN-based accumulated total inflow model are better than that of the ANFIS-based model because of the real-time recurrent deterministic routing mechanism of RTRLNN. Simulations show that the RTRLNN-based model with coupled heuristic inputs (RTRLNN-CHI, average error percentage (AEP)/average forecast lead-time (AFLT): 6.3%/49 h) can achieve better prediction than the model with non-heuristic inputs (AEP of RTRLNN-NHI and ANFIS-NHI: 15.2%/31.8%) because of the full consideration of real-time hydrological initial/boundary conditions. Besides, the RTRLNN-CHI model can promote the forecasted lead-time above 49 h with less than 10% of AEP which can overcome the previous forecasted limits of 6-h AFLT with above 20%-40% of AEP. [ABSTRACT FROM AUTHOR]

Gas compressibility factor is an important thermodynamic property introduced to modify the ideal gas law to account for the real gas behavior. Precise determination of this property is the top priority for many aspects of chemical and petroleum engineering applications. After the publication of the results of the experiments conducted by Standing and Katz in 1942, their proposed chart has become an industry standard, and since then many authors have tried to develop new models by fitting their parameters to that chart. Lack of sufficient accuracy, especially near specific boundaries, as expected for applications like in numerical reservoir simulators, and the iterative nature of procedures are the most common problems of these suggested models. To defeat such problems, in this research a novel type of model is developed on the basis of fuzzy inference system. The error analysis of the generated model compared with the widely accepted Dranchuk and Abou-Kassem correlation, the artificial neural network model proposed by Kamyab and colleagues and several other well-known models demonstrate that fuzzy logic can reach much higher precisions than most of the current methods. [ABSTRACT FROM PUBLISHER]

Due to low oxygen solubility and mechanical stirring limitations of a bioreactor, ensuring an adequate oxygen supply during a recombinant Escherichia coli cultivation is a major challenge in process control. Under the light of this fact, a fuzzy dissolved oxygen controller was developed, taking into account a decision tree algorithm presented in the literature, and implemented in the supervision software SUPERSYS_HCDC. The algorithm was coded in MATLAB with its membership function parameters determined using an Adaptive Network-Based Fuzzy Inference System tool. The controller was composed of three independent fuzzy inference systems: Princ1 and Princ2 assessed whether there would be an increment or a reduction in air and oxygen flow rates (respectively), whilst Delta estimated the size of these variations. To test the controller, simulations with a neural network model and E. coli cultivations were conducted. The fuzzification of the decision tree was successful, resulting in smoothing of air and oxygen flow rates and, hence, in an attenuation of dissolved oxygen oscillations. Statistically, the average standard deviation of the fuzzy controller was 2.45 times lower than the decision tree (9.48%). Results point toward an increase in the flow meter lifespan and a possible reduction of the metabolic stress suffered by E. coli during the cultivation. [ABSTRACT FROM AUTHOR]

The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures. [ABSTRACT FROM AUTHOR]