Fuzzy Inference System

There are a lot of knowledge in the Web, but there is no technological way to extract them. The bulk of knowledge is embedded in texts, and machine text processing is so inefficient that it is necessary to use Semantic Web technologies [1]. Working with ontologies (part of the Semantic Web) is convenient, but the process of creating ontologies is still more of a manual work than an automatic process. This paper proposes to generate IF..THEN rules from raw texts (from sentences) in the Web, and then perform logical inference based on these rules. Moreover, semantic processing is proposed to be applied to the IF part and the THEN part, and not to the entire raw text, generating an ontology from it. This method of generating rules and logical inference is being implemented in the Keyword Search Engine Enriched by Expert System Features [2], which will allow us to obtain expert assessments from many useful texts in the Web.

The controllability of torque in an induction motor without any peak overshoot and less ripples with good transient and steady state responses form the main criteria in the designing of a controller. Though, PI controller is able to achieve these but with certain drawbacks. The gains can not be increased beyond certain limit so as to have an improved response. Moreover, it introduces non linearity into the system making it more complex for analysis. Also it deteriorates the controller performance. With the advent of artificial intelligent techniques, these drawbacks can be mitigated. One such technique is the use of Fuzzy Logic in the design of controller either independently or in hybrid with PI controller. This paper proposes a unique set of fuzzy logics for the speed controller design to be used in vector controlled three phase induction motor. The results obtained from the model using proposed Fuzzy Logic Controller and PI Controller are compared. It can be concluded that use of Fuzzy logic improves and smoothens out the ripples in the motor torque and stator currents. It also facilitates in limiting the magnitude of the torque and current values within the specified range in any kind of disturbance, either provided by the speed removal or by sudden application and removal of load torque. This has been verified through the simulation results of the model built completely in a MATLAB/SIMULINK environment.

In this article, the aim is define fuzzy agents with dynamic personality for the simulation of human behavior. Fuzzy sets are defined for personality traits and facets and the concise representation of personality knowledge is processed in fuzzy logic. This work is based on the personality knowledge as distilled from psychology [Ören and Ghasem-Aghaee 2003a].

Fuzzy logic systems (FLS) provide a strong paradigm to model, reason, and make decisions in environments of uncertainty, imprecision, or incomplete information. Unlike classical Boolean logic with truth values limited to 0 or 1, fuzzy logic enables partial membership and continuous transition of truth values, thus enabling one to model real-world phenomena in a more proximate manner. This paper is a comprehensive overview of fuzzy logic systems: from the theoretical foundationsfuzzy sets, membership functions, inference engines, and defuzzification methods-to other fuzzy logic models (Type-1, Type-2, Mamdani, Takagi-Sugeno). We cover recent developments in hybrid systems such as neuro-fuzzy, evolving fuzzy systems, and fuzzy rule-based systems optimized using heuristic and evolutionary methods. Through case studies of robotics (robot navigation in mobile robots and swarm robotics), bioenergy technology, wastewater treatment, hydraulic and pneumatic systems, and fault-tolerant control, we illustrate in practice the application of fuzzy logic, its performance, and compromises. Compromises among gains in interpretability, flexibility in handling uncertainty, and adaptability are balanced against computational complexity, rule base explosion, expert-dependence, and realtime implementation constraints. Trends and developments covered include advancements in Type-2 fuzzy logic, meta-heuristics parameter tuning, and uncertainty quantification for safety-critical tasks. Our view is that with increasingly complex applications (IoT, autonomous systems, environment monitoring), fuzzy logic systems-especially advanced ones-are becoming increasingly relevant. This paper aims to assist researchers in making the right choice of FLS architectures, designing efficient inference and defuzzification methods, and navigate through some of the current challenges in applying fuzzy logic to modern complex systems.

In this study, stiffness modulus parameters of asphalt concrete were determined experimentally for different temperature and exposure times. The stiffness modules were calculated according to Nijboer stiffness module. Basic physical properties and the quantity of bitumen of asphalt core samples were designated for determining the stiffness modules. The samples were exposed to 17 o C (reference temperature), 30, 40 and 50 o C temperatures for 1.5, 3, 4.5 and 6 hours and then Marhall Stability test were done for each samples. By using the test results a prediction model with Sugeno type based on the adaptive neuron-fuzzy inference system was alternatively developed to calculate the stiffness modules of asphalt core samples. As a result, it was seen that the developed prediction model could be used as a prediction model for unperformed situations which aren't suitable for experiments.

In this study, stiffness modulus parameters of asphalt concrete were determined experimentally for different temperature and exposure times. The stiffness modules were calculated according to Nijboer stiffness module. Basic physical properties and the quantity of bitumen of asphalt core samples were designated for determining the stiffness modules. The samples were exposed to 17 °C (reference temperature), 30, 40 and 50 °C temperatures for 1.5, 3, 4.5 and 6 h respectively and then Marhall Stability tests were done for each samples. By using the test results a prediction model with Sugeno type based on the adaptive neuron-fuzzy inference system (ANFIS) was alternatively developed to predict the stiffness modules of asphalt core samples. As a result, it was seen that the developed prediction model could be used as a prediction model for unperformed situations which are not suitable for experiments.

Decision making theories such as Fuzzy-Trace Theory (FTT) suggest that individuals tend to rely on gist, or bottom-line meaning, in the text when making decisions. In this work, we delineate the process of developing GisPy, an opensource tool in Python for measuring the Gist Inference Score (GIS) in text. Evaluation of GisPy on documents in three benchmarks from the news and scientific text domains demonstrates that scores generated by our tool significantly distinguish low vs. high gist documents.

Today, neural network models are widely used to predict whether a person will develop diabetes in the future. However, for fuzzy inference engine and Adaptive Network-based Fuzzy Inference System (ANFIS), it costs a lot when the number of features is large, and the model will be more explainable if we use feature selection methods. In this paper, we modify the ANFIS model, combine ANFIS and neural network, and propose the ANFIS-NN model. We use SMOTE to address the imbalance between different classes, and use RFE and Casual Inference to do feature selection work. Then, we train an ANFIS model, and use a 5-layer neural network to replace the last layers to improve prediction accuracy. Data comparison experiments shows that our models get 0.9812 on accuracy, 0.9790 on G-mean, 0.9776 on F1 score on PIMA dataset using Casual Inference feature selection method, much better than traditional ANFIS; and our model works better on other diabetes datasets. The ANFIS-NN proposed in this paper c...

The flow in sewers is a complete three phase flow (air, water and sediment). The mechanism of sediment transport in sewers is very important. In other words, the passing flow must able to wash deposited sediments and the design should be done in an economic and optimized way. In this study, the sediment transport process in sewers is simulated using a hybrid model. In other words, using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Particle Swarm Optimization (PSO) algorithm a hybrid algorithm (ANFIS-PSO) is developed for predicting the Froude number of three phase flows. This inference system is a set of if-then rules which is able to approximate non-linear functions. In this model, PSO is employed for increasing the ANFIS efficiency by adjusting membership functions as well as minimizing error values. In fact, the PSO algorithm is considered as an evolutionary computational method for optimizing the process continues and discontinues decision making functions. Addition...

When assessing the risk of power transformer (PT) failure, routine test results can be used to estimate the condition of each unit and group those that share similar issues. Since the transformer is one of the most expensive and essential components within a power system, this study aims to present a methodology for grouping PTs with similar health problems and Consequences of Failure (CoF). This document presents two grouping options to identify which provide greater benefit in defining the maintenance scheme. To evaluate the CoF, the consequence factor found in the United Kingdom Distribution Network Operators (DNO) methodology is used, which considers factors such as overload to other assets, average load supplied by the unit, critical loads fed, oil volume, proximity to other buildings, and unavailability penalties, among others. This approach is evaluated on a fleet of 39 power transformers. The results show that the combination of the Health Index (HI) and the Consequence Factor leads to consistent indices for risk assessment of the units. Al evaluar el riesgo de falla de los transformadores de potencia (PT), se pueden utilizar resultados de pruebas de rutina para estimar el estado de cada unidad y agrupar aquellas con problemas similares. Dado que el transformador es uno de los componentes más caros e importantes dentro de un sistema de potencia, el objetivo de este estudio es presentar una metodología para agrupar PTs con problemas de salud y consecuencias de falla (CoF) similares. En este documento se presentan dos opciones de agrupación para identificar cuál ofrece mayores beneficios a la hora de definir el esquema de mantenimiento. Para evaluar las CoF, se utiliza el factor de consecuencia encontrado en la metodología de gestores de redes de distribución (DNO) del Reino Unido, que considera factores como sobrecargas a otros activos, carga media suministrada por la unidad, cargas críticas alimentadas, volumen de aceite, proximidad a otros edificios y penalizaciones por indisponibilidad, entre otros. Esta propuesta se evalúa en una flota de 39 transformadores de potencia y los resultados muestran que la combinación del índice de salud (HI) y el factor de consecuencia conduce a índices consistentes para la evaluación del riesgo de las unidades.

This paper presents the construction of a system suitable for student diagnosis with the use of neurofuzzy techniques. To be more specific, the extent of usefulness of Adaptive NeuroFuzzy Inference Systems (ANFIS) is examined for modeling automated evaluation of the answers and progress of deaf students' that possess basic knowledge of the English language and computer skills, within a virtual e-learning environment. The performance of the specific methods is evaluated with the correlation factor between the neural networks' response values and the real value data as well as the error between the neural networks' estimate values and the real value data during its training process and afterwards with unknown data that weren't used in the training process. The system was trained through data extracted from an educational project called ENFORA.

Objective: The construction industry has been increasingly criticized for its poor sustainability performance in recent decades, creating a chance for the sector to play a key role in global sustainability efforts. Rapid technological advancements and increasing construction project complexities have driven the need for flexible, sustainability-focused project management frameworks. This study introduces a fuzzy inference system designed to evaluate construction project sustainability, built on insights from extensive literature and expert input.
Methods: To design the proposed model, the system inputs—criteria for evaluating the sustainability level of construction projects at various layers—were first identified. Next, the necessary if-then rules were developed based on expert opinions. The system output was determined in alignment with the research’s final objective. By offering a comprehensive assessment of construction project sustainability, the model enables organizations to identify their strengths and weaknesses, assess their current position, and make informed decisions to enhance their sustainable performance.
Results: The output of the research includes a detailed analysis of the sustainability performance of construction projects. The designed model, along with its measurement tools, provides an opportunity for leaders and managers in the construction industry who are concerned about sustainability to gradually enhance their sustainability status and advance the sustainability level of projects. This model consists of three subsystems named the Direction, Execution, and Results subsystems. The aforementioned subsystems are the result of a literature review and are considered inputs to the final level of the model.
Conclusion: The designed model serves as a tool to identify and implement improvement methods and potential areas for project advancement from a sustainability perspective. By utilizing this model, the quality of project execution in line with sustainability indicators, while addressing all three dimensions—economic, social, and environmental—improves continuously and proportionately

This paper describes an architecture for predicting the price of cryptocurrencies for the next seven days using the Adaptive Network Based Fuzzy Inference System (ANFIS). Historical data of cryptocurrencies and indexes that are considered are Bitcoin (BTC), Ethereum (ETH), Bitcoin Dominance (BTC.D), and Ethereum Dominance (ETH.D) in a daily timeframe. The methods used to teach the data are hybrid and backpropagation algorithms, as well as grid partition, subtractive clustering, and Fuzzy C-means clustering (FCM) algorithms, which are used in data clustering. The architectural performance designed in this paper has been compared with different inputs and neural network models in terms of statistical evaluation criteria. Finally, the proposed method can predict the price of digital currencies in a short time.

In this work, the compressive strength of concrete made from recycled aggregate is studied and an intelligent prediction is proposed by using a novel artificial neural network (ANN), which utilizes a sigmoid function and enables the proposal of closed-form equations. An extensive literature search was conducted, which gave rise to 476 data points containing cement, sand, aggregates, recycled aggregates of fine to coarse texture, water, and plasticizer as the constituents of the concrete and the input variables of the intelligent model. The compressive strength (fc) of the recycled aggregate concrete (RAC), which was studied through multiple experiments, was the output variable of the model. The data points of concrete strength collected through literature show a consistent and sustained strength improvement with the increase in the recycled aggregate proportions. However, the outcome of the concrete compressive strength predictive model shows remarkable performance indices as follows; r is 0.99 and 0.99, R 2 is 0.98 and 0.97, MSE is 28.67% and 44.64%, RMSE is 5.35% and 6.68%, MAE is 4.12% and 5.01%, and MAPE is 12.73% and 13.83% for the model training and testing respectively. These results compared well with previous studies conducted on RAC with less data, different activation functions, and different techniques. Generally, the closed-form equation, which performed at an average accuracy of 97.5% with an internal consistency of 99%, has shown its potential to be applied in RAC design and construction activities for a sustainable performance evaluation of recycled aggregate concrete.

The partner village development program (PPDM) has been carried out about the production of ant sugar from coconut sap in the village of Wonosobo, Banyuwangi Regency as a college partner. This activity is intended to assist government programs in the development of coconut sugar products into Healthy Coconut Sugar (Helicos) products. This is expected to be the main icon and driver of the Village to become the center of Helicos products. The service program activities are carried out on April 16 - July 4, 2018. In the implementation of this activity, participants who work as print palm sugar producers participated. With this activity, partners get new understanding and skills in making innovation-based palm sugar. Community service is carried out by coaching partners through diversification of coconut sugar products into Helicos ants, improving product quality by applying hygine-sanitation, K3 during harvesting, processing and storage. The results of the dedication program can be see...

The death rate is caused by breast cancer in women is increasingly high and growing. A number of people are getting to lose this part of their body due to late diagnosis of this disease. This therefore requires the development of an efficient and accurate diagnosis approach that will aid providing the knowledge of the type of breast cancer type and severity in order to reduce the mortality rate through the disease. This need serves as the major motivation for this work. In this paper, we proposed a fuzzy expert system for diagnosis of and treatment recommendation of breast cancer problems which provide physicians and patients with information of the cancer type and treatment recommendation. The application was designed using JAVA programming language, MATLAB and SQLite database engine. This application permits update of new information as a means of knowledge. The evaluation showed that the inclusion of the fuzzy inference system improved the accuracy and precision of the system from 0.8 to 0.9. The system is user-friendly and has high level of acceptability from the validation conducted at the end of the research.

The Interval Type-2 Fuzzy Logic Control (IT2FLC) utilizes a genetic algorithm (GA), known as the Genetics Interval Type-2 Fuzzy Network (GIT2FS), to optimize the fuzzy parameters, including fuzzy functions for membership and fuzzy regulation bases. After a brief discussion of the genetic fuzzy system GFS, the suggested design is described. Type reductions and defuzzification are included in the output processing of interval type-2 fuzzy logic circuits. Although researchers have recently developed numerous effective type reduction techniques, there are currently no practical plans to enhance the output of defuzzification. The kind of interval type-2 fuzzy set is reduced using the type reduction algorithm presented in this paper, which also produces the best defuzzified output from the type-reduced set. The planned type reduction is also carried out offline (in other words, the controller has been reduced to type-1 in practical applications). It greatly lowers the computational expense and makes it easier, actually, to develop controllers. Problems with truck backing control are used to show the viability of the suggested approach. The study showed that, in terms of speed, computational complexity, and resilience, the suggested technique performs better than typical IT2-FLCs.

Nowadays, construction projects became more complex, where the responsibility of construction manager is to control and plan the project resources in a professional way to handover the project in terms of time, cost, and quality. The aim of this study is to analyze the affect of the long experience of the construction manager on project success; it aims to identify the capability skills that the construction manager should have to complete the project on time. The finding of this study is to help in understanding the factors that influence the project success through the long experience of the construction manager.

Wind energy power (WEP) is currently one of the generating technologies that could be implemented massively due to its low environmental impact and abundant resources. However, the availability of the wind always changes depending on the weather condition, such that the power system should be designed properly to adopt the intermittence of the power injection of WEP. Therefore, the wind speed forecasting is very prominent to be performed to ensure the WEP could be incorporated into the existing power system. In this paper, the proposed methods to predict the wind speed are developed based on the artificial intelligence methods i.e. Adaptive Neuro Fuzzy Inference System (ANFIS) and based on the conventional methods i.e. Auto Regressive Integrated Moving Average (ARIMA). Case study in Malang city of Indonesia has been taken to compare the performances of both methods. Some membership functions (MF) have been studied to show the performance of ANFIS. The mean absolute error (MAE) and root mean square error (RMSE) have been used as standard statistical metrics to measure the performance of ANFIS and ARIMA methods. The results show that the optimal ANFIS architecture was obtained with 85% training data and 15% testing data by using the Generalized Bell membership function with MAE of 2.1354 km/h and RMSE of 2.6333 km/h. In addition, the wind forecasting result using ARIMA has been obtained with MAE of 2.8383 km/h and RMSE of 3.4628 km/h. The ANFIS method offers better performance than ARIMA does for short-term forecasting of wind speed in terms of MAE and RMSE values.

Extracting fuzzy system rules from experimental data by means of clustering constitutes at present a commonly used technique. Intense studies in the field have shown that this method leads to a significant reduction in the system's complexity, maintaining at the same time its high modelling performance. In this paper a clustering algorithm, based on a kernel density gradient estima-tion procedure applied for fuzzy system identification, is presented. The method is unsupervised, fully automatic and does not need any requirement concerning the assumed number of data clusters. The results of experimental evaluation show that the algorithm under investigation is in most cases superior over the standard subtractive clustering technique frequently applied in similar types of problems.

Forecasting is a common thing to capture events in future based on previous information. However, some classical time-series methods, including moving average (MA), autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), and simple exponential smoothing (SES), have limitations in predicting nonlinear time-series data. Therefore, this paper aims to utilize the adaptive neuro-fuzzy inference system (ANFIS) model, a combination of the fuzzy inference system (FIS) and neural network architecture to forecast a nonlinear rainfall problem. This model can capture the non-linear data, adaptation capability, and speedy learning capacity. We used the data consisting of temperature (ºC), humidity (%), and wind speed (km/hour) as input variables and rainfall (millimeter) as an output variable at two stations and one rain post in Aceh Besar District, from January 2009 to December 2019. The results demonstrated that ANFIS with generalized Bell (gBell) membership function on epoch 10 can successfully conduct rainfall forecasting in Aceh Besar District with the best-predicted value. The mean absolute percentage error (MAPE) of the prediction at the Meteorology, Climatology, and Geophysics Agency (MCGA) Station or Badan Meteorologi, Klimatologi dan Geofisika (BMKG) Indrapuri is 6.73% for 80% of the training dataset and 20% of the testing dataset.

Tilapia fish farming faces growing challenges from climate variability, environmental degradation, and the urgent demand for sustainable food production. However, traditional water quality monitoring methods remain manual and reactive, often resulting in compromised fish health and reduced farm productivity. Addressing this need, this study designed and developed a water quality monitoring system utilizing the internet of things (IoT) and embedded systems to enable real-time, proactive management. Guided by the software development life cycle (SDLC), the methodology focused on planning and analysis, system design and development, and testing and evaluation. The system integrates key water quality sensors, including pH, temperature, dissolved oxygen (DO), and electrical conductivity (EC), identified as critical parameters affecting tilapia health. These sensors were interfaced with Arduino Nano and ESP32 Dev Kit microcontrollers, forming the sensing layer of the system. Sensor data were transmitted to the ThingSpeak IoT platform for real-time visualization and storage. Validation results revealed a low mean absolute percentage error (MAPE), indicating an acceptable sensor performance. User evaluation, based on the technology acceptance model (TAM), indicated that the system was perceived as useful, user-friendly, and valuable for aquaculture management. Overall, the system enables real-time water quality monitoring, supporting a more responsive and sustainable environment for tilapia fish farming.

Energy production and distributing have critical importance for all countries especially developing countries. Studies about energy consumption, distributing and planning have much importance at the present day. In order to manage any power plant or take precautions about energy subject, many kinds of observations are used for short, mid and long term forecasting. Especially long term forecasting is in need to plan and carry on future energy demand and investment such as size of energy plant and location. Long term forecasting often includes power consumption data for past years, national incoming per year, rates of civilization, increasing population rates and moreover economical parameters. Long term forecasting data vary from one month to several years. Some of the forecasting models use mathematical formulas and statistical models such as correlation and regression models. In this study, artificial intelligence is used to forecast long term energy demand. Artificial intelligences are widely used for engineering problems to solve and obtain valid solutions. Adaptive neural fuzzy inference system is one of the most famous artificial intelligence methods and has been widely used in literature. In addition to numerical inputs, Adaptive neural fuzzy inference system has linguistics inputs such as good, bad and ugly. Adaptive neural fuzzy inference system is used to obtain long term forecasting results and the results are compared to mathematical methods to show validity and error levels. In order to show error levels, mean absolute error and mean absolute error percentage are used. Mean absolute error and mean absolute error percentages are very common and practical methods in literature. The obtained error results, from 2003 to 2025, mean absolute error and mean absolute percentage error are 1.504313 and 0.82439, respectively. Success of Adaptive neural fuzzy inference system for energy demand forecasting is 99.17%.

This paper presents the analysis of actual oscillographic records with high impedance faults (HIFs) upon various contact surfaces in order to identify the main characteristics of such fault in both time and wavelet domains. The features of HIFs extracted by means of the wavelet coefficient energies of the maximal overlap discrete wavelet transform (MODWT) have been used for developing a new detection method to yield results in satisfactory agreement with real applications. The proposed HIF detection method was installed at a local utility substation and good results have been obtained.

One of the difficulties to improve on the fly writer-dependent handwriting recognition systems is the lack of data available at the beginning of the adapting phase. In this paper we explore three possible strategies to generate synthetic handwriting characters from few samples of a writer. We explore in this paper both classical image distortions and two original ways to generate on-line handwritten characters: distortions based on specificities of the on-line handwriting and a generation based on analogical proportion. The experimentations show that these three approaches generate different distortions which are complementary. Indeed the combination of them allows the achievement, using only 4 original characters for the learning phase, of a mean of 91.3% of recognition rate for 12 writers.

In our previous works, a recognition system named Re-sifCar was designed specifically for on-line handwritten character recognition. This system is based on an explicit modeling by hierarchical fuzzy rules. Thus, it is understandable an optimizable after the learning stage. We present in this article a new classifier that is an extension of Resif-Car. Indeed it tries to combine ResifCar's advantages with a generic aspect to handle different recognition problems. This new hybrid system combines two complementary levels. The first one uses a robust modeling by an intrinsic fuzzy clustering of each class and determines their confusing areas. The second level, based on fuzzy decision trees, operates a progressive discrimination inside these areas. Both levels are formalized by fuzzy inference systems organized hierarchically and fused for final decision. Experiments were conducted on the one hand on classical benchmarks and on the other hand on on-line handwritten digits and lower-case letters. For all of these cases, the classifier achieves good recognition rates without final optimization.

Composite indices are used in many of the traditional approaches to measure risk to natural hazards. However, such indices are often built assuming linear interdependencies between the aggregated components, comprising in this way any realistic representation of the intricate and unseen processes that are behind each component along with their mutual influences when it comes to encapsulate a complex reality in a single construct. In this paper we used a Fuzzy Inference Systems type Mamdami to aggregate physical seismic risk and social vulnerability indicators without assuming linearity. The aggregation is made by establishing rules (if-then type) over the indicators in order to get a fuzzy composite index. We generated solution surfaces representing the outcome of a widely known linear aggregation method and the fuzzy system. Finally we performed a qualitative comparison between both to highlight their differences and possible consequences in terms of risk management

In this research, the researchers have managed to design a model to investigate the current trend of stock price of the "IRAN KHODRO corporation" at Tehran Stock Exchange by utilizing an Adaptive Neuro - Fuzzy Inference system. For the Longterm Period, a Neuro-Fuzzy with two Triangular membership functions and four independent Variables including trade volume, Dividend Per Share (DPS), Price to Earning Ratio (P/E), and also closing Price and Stock Price fluctuation as an dependent variable are selected as an optimal model. For the short-term Period, a neureo – fuzzy model with two triangular membership functions for the first quarter of a year, two trapezoidal membership functions for the Second quarter of a year, two Gaussian combination membership functions for the third quarter of a year and two trapezoidal membership functions for the fourth quarter of a year were selected as an optimal model for the stock price forecasting. In addition, three independent variables inc...

The vertical electrical soundings on the Matam region phosphates deposits are interpreted by inversion. To retrieve lithology from the obtained resistivities, mechanical drilling was performed to compare directly the lithologies and the geoelectrical resistivities. The obtained relation is not simple and is not sufficient to interpret all the VES. This situation motivated the collection of supplementary information from cyclostratigraphy. Geoelectrical and cyclostratigraphic information is combined using fuzzy logic techniques to build a fuzzy inference system. The obtained results seem to be consistent with the stratigraphy of the investigated region and allow retrieving lithological succession. Further investigations are necessary for more accurate thickness determination.

The challenge for our paper consists in controlling the performance of the future state of a microgrid with energy produced from renewable energy sources. The added value of this proposal consists in identifying the most used criteria, related to each modeling step, able to lead us to an optimal neural network forecasting tool. In order to underline the effects of users' decision making on the forecasting performance, in the second part of the article, two Adaptive Neuro-Fuzzy Inference System (ANFIS) models are tested and evaluated. Several scenarios are built by changing: the prediction time horizon (Scenario 1) and the shape of membership functions (Scenario 2).

The challenge for our paper consists in controlling the performance of the future state of a microgrid with energy produced from renewable energy sources. The added value of this proposal consists in identifying the most used criteria, related to each modeling step, able to lead us to an optimal neural network forecasting tool. In order to underline the effects of users' decision making on the forecasting performance, in the second part of the article, two Adaptive Neuro-Fuzzy Inference System (ANFIS) models are tested and evaluated. Several scenarios are built by changing: the prediction time horizon (Scenario 1) and the shape of membership functions (Scenario 2).

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

The paper presents the development of a fuzzy logic inference technique for the hierarchical level one HL1 reliability for a power system. The inference process is rule based where fuzzy values are assigned to the fuzzy variables. The average unit size, system ratio, f o r d outage rate and the load factor constitute the left hand side of the fuzzy rule whereas the loss of load expectation is the right hand side consequent. The suggested technique for inferring rather than calculating the loss of load expectation has the advantage of handling the uncertainty that may be associated with the system data. This approach shows satisfactory results.

Peak ground acceleration (PGA) estimates have been calculated in order to predict the devastation potential resulting from earthquakes in reconstruction sites. In this research, a training algorithm based on gradient descent were developed and employed by using strong ground motion records. The Artificial Neural Networks (ANN) algorithm indicated that the fitting between the predicted strong ground motion by the networks and the observed PGA values were able to yield high correlation coefficients of 0.78 for PGA. We attempt to provide a suitable prediction of the large acceleration peak from ground gravity acceleration in different areas. Methods are defined by using fuzzy inference systems based on adaptive networks, feed-forward neural networks (FFBP)by four basic parameters as input variables which influence an earthquake in regional studied. The affected indices of an earthquake include the moment magnitude, rupture distance, fault mechanism and site class. The ANFIS network --with an average error of 0.012 --is a more precise network than FFBP neural networks. The FFBP network has a mean square error of 0.017 accordingly. Nonetheless, these two networks can have a suitable estimation of probable acceleration peaks (PGA) in this area.

Background and objective The combined-cycle power plants are one of the important types of power plants in the world with high occupational accidents. To reduce the costs for occupational accidents and reduce the lost days, finding the root causes of accidents in these industries is necessary. This study aims to assess the risk of hydrogen gas leakage from the chlorination unit of a combined-cycle power plant with once-through cooling system using the Bow-tie risk assessment method. Method At first, the hydrogen gas leakage from the chlorination unit of a combined-cycle power plant in Iran was selected as the scenario for probabilistic risk assessment. For finding the causes and consequences of the scenario, the bow-tie method was used. Results We identified 44 basic events, three safety barriers that prevent the occurrence of the desired scenario (congestion, immediate ignition, and delayed ignition), and six consequences including flash fire, jet fire, fire ball, pool fire, explosion, and safe release of hydrogen gas. The probability of hydrogen leakage from the chlorination unit was 1.51×10-1 %, and the most likely consequence was "explosion/jet fire/flash fire," with a probability of 4.89×10-2 %. Conclusion We identified the causes, safety barriers, and potential consequences of hydrogen gas leakage from the chlorination unit of a combined-cycle power plant. The malfunction of electrolyzers is the most significant cause and explosion/jet fire/flash fire is the most important consequence of hydrogen leakage.

The main objective of this study was to investigate the relationships between the use of smart technology (mobile phones) and the implicit (tacit) and explicit safety knowledge of employees and their propensity to follow safe practices at work. A survey was performed with seven constructs: (a) use of mobile technology; (b) tacit safety knowledge; (c) explicit safety knowledge of unsafe behaviors; (d) attitudes toward safety: emotional aspects; (e) safety culture: behavioral and psychological aspects of work; (f) safety culture: aspects of work; (g) safety culture: regulations at work. Workers from three manufacturing companies located in southeastern Poland completed a paper-based survey. The results revealed that using mobile technology positively influenced the explicit safety knowledge of employees, as well as their assessed safety culture, in terms of behavioral aspects and their attitudes toward safety expressed through the psychological aspects of safety culture.

Human socio-cultural behavior (HSCB) modeling has received much attention in attempts to successfully understand the effects of social and cultural factors on human behavior. The principal aim of HSCB modeling is to better organize and control the human terrain during nonconventional warfare and other missions. The intent of this study is to review and classify the literature based on the applications of different kinds of data for modeling HSCB and highlight the soft-computing methodologies suitable for HSCB event/incident data that have been applied successfully to crime data.

Artificial intelligence (AI) is increasingly embedded within smart sensors and actuators to enable adaptive,
autonomous, and predictive control in mechatronic systems. This review analyzes over 25 peer-reviewed studies,
classifying methodologies by AI techniques, integration depth, and application context. Citation summaries
highlight trends and future directions in robotics, manufacturing, soft systems, and embedded sensing. The study
identifies research gaps, discusses deployment barriers (edge AI, standardization), and proposes an integration
framework for next-generation AI-powered mechatronics.

Military technological advancements are crucial for meeting evolving operational needs. Submarines are essential naval assets due to their difficulty in detection. Selecting appropriate equipment for mission efficiency is essential, and automating these tasks with a Decision Support System (DSS) is vital. Air Independent propulsion (AIP) is critical for covert submarine operations, allowing propulsion underwater without surfacing for oxygen. Choosing the right AIP system is challenging because it requires balancing operational efficiency, economic cost-effectiveness, and environmental consequences, all of which entail complex factors that are difficult to quantify or compare. The developed DSS is based on a two-stage Fuzzy Mamdani Model (FMM), applied to perform inference over a submarine AIP. Three reference values were obtained in the first stage to aid operational, economic, and environmental decision-making. In the second stage, these three values create a recommendation value using a percentage system selection. To prove its effectiveness, the DSS was used to evaluate four types of AIPs to establish the level of recommendation and the values of the subcategories in the initial phase. Experts from the Portuguese Navy (PoN) assisted with the system development by defining decision-making rules. The FMM offers advantages over traditional decision-making models by effectively managing vague and qualitative data, enabling reasoning that reflects expert judgment instead of relying on deterministic models. This DSS not only enhances the accuracy and transparency of AIP selection but also provides a scalable and adaptable framework applicable to various complex decision-making scenarios in other engineering applications and fields.

Safety and comfort are two important aspects that must be achieved at the time of driving. The level of safety when driving can be improved by reducing driver (human) error. An auxiliary device is required by the driver to avoid an accident. Advanced Driver Assistance Systems (ADASs) are systems designed to help the driver in the driving process. The Overtaking Assistance System (OAS) is a subsystem of an ADAS that functions by assisting the driver in the overtaking process. This paper presents a decision-making system based on fuzzy logic for the OAS. The inputs in a decision-making system are the distance of the ego vehicle and the vehicle that will be overtaken as well as the distance of the ego vehicle and the vehicle in the other lane. The decisions involved in a decision-making system concern the vehicle performing the approaching, tailgating, and overtaking. The results from hardware simulation using a remote control car show that the decision-making system algorithm can work according to the design.

This paper highlights usability of statistical fuzzy inference systems based on PCA for tacit knowledge modeling in Biomass energy. Tacit knowledge is the key to management of ecological innovations in electric utilities of biomass power plants. However, tacit knowledge in the process of information gathering has not been implemented in a formalized way. Although the Sugeno defuzzification method is considered to be the most computationally effective, there is uncertainty about the defuzzified output, since it generates singleton fuzzy values objectively and is not well evaluated. A methodology enhancing the ability of statistical fuzzy inference systems for the improvement of tacit knowledge modeling. This has been exploited in the process of the new approach. The statistical inference system has been used directly integrated with the principal component analyzer, fuzzy inference engine, knowledge base and user interface. The system has been evaluated by a sub field of power systems domain of electricity marketing in biomass power plants. Approach evaluates performance of Biomass energy available for electricity generation. The has been built based on the proposed model and tacit data sets in electricity generation to be used to verify the accuracy of the model and the prototype. The electric utility assessment tool based on a questionnaire to classify electric utilities (agricultural residues, Forest waste, and Household waste) in percentages and identify electric utility performance index in biomass power plants. The experiments were conducted to investigate performance of PCA based Statistical Fuzzy Inference System based on a pilot survey. Output of the system is for computing biomass performance index based on fuzzy output values (agricultural residues, forest waste, household waste) has been generated by PCA based defuzzification process. The accuracy of the PCA based Statistical Fuzzy Inference System approach is 97%.

The successful implementation of Smart Grids heavily relies on energy efficiency, particularly through the Advanced Metering Infrastructure (AMI) and Smart Electricity Meters (SEM). However, cyber-attacks pose a threat to SEM, with electricity theft being a primary motivation. Despite the valuable data provided by SEM for analytical purposes, existing methods to identify theft involve cumbersome and costly on-site inspections. This research proposes an electricity theft detection model using the Long Short-Term Memory (LSTM) network. The model employs a collective anomaly approach, defining prediction errors through a threshold and forecast horizon. Suspicious consumption profiles are analysed, and a fuzzy inference system (FIS) implemented in MATLAB 2021b is used to model security risks based on these profiles. The study utilizes energy consumption data from four diverse consumer profiles (consumers 1, 2, 3, and 4) to develop consumer-specific LSTM models for detection and an FIS model for confirmation. Tampered consumer data is identified and confirmed based on selected AMI parameters. While all consumers exhibit suspicious profiles at times, only consumers 2 and 3 are confirmed as engaging in electricity theft. This research provides a robust approach to detecting and verifying fraudulent consumption profiles within the context of AMI, offering a more reliable dimension to theft detection and confirmation.

The massive use of social media makes people take actions that have a negative impact on cyberspace, such as creating fake accounts that aim to commit crimes such as spam and fraud to spread false information. Fake accounts are difficult to detect in the traditional way because fake accounts always use photos, names, and unreal information, there are several criteria that can identify a fake account such as no information, few followers, and minimal activity. In the traditional model, it is difficult to detect fake accounts on many Twitters social media accounts, so the application of the deep learning model with the convolutional neural network (CNN) algorithm and the application of deep learning can help detect fake accounts. This study will use data on Twitter social media so that this research produces good accuracy for the scenarios described at the methodology stage. This research produces an accuracy of 86% for the deep learning model with the CNN algorithm, and with the traditional model, it produces an accuracy of 51% while the use of transfer learning produces an accuracy of 93.9%.

Endeavour has been made in this research work using experimental data for constructing a fuzzy inference model based on the Mamdani approach to prognosticate the shrinkage and mass per unit area of a single jersey cotton knitted fabric. To control the dimensional stability of the cotton knitted fabric in advance, an artifi cial intelligent system is required in the knitting industry which simulates all product and process variables and is able to give human-like decisions in advance. The most important controlling parameters of knitted fabric properties such as stitch length, yarn count and overfeed percentage in stenter were considered as input variables, and mass per unit area, lengthwise shrinkage and widthwise shrinkage as output variables. Overall, 35 experiments were conducted to construct the model, varying diff erent parameters. The applicability of the model was validated by comparing the results from 15 newly conducted experiments. The coeffi cient of determination of predicted and actual data for mass per unit area, lengthwise shrinkage and widthwise shrinkage were 0.97, 0.99 and 0.99, respectively which validates the model relatively eff ectively for an industrial application. The proposed model can assist a fabric manufacturer by taking a decision in selecting knitting and fi nishing parameters prior to producing the fabric. Moreover, it can reduce the time and energy required, and waste produced in the process by skipping the sample development step before bulk production.

The paper presents and analyses rapid prototyping methods for the dynamic emulation of mechanical loads. Approaches can be applied in design, testing and validation of the mechatronic systems propelled by electric drives. Actual system (prototype) is replaced by the torque controlled electromechanical load, for which the required torque is calculated through the closed-loop control algorithm. The active load is connected mechanically to the drive shaft, using the clutch. Also possible applications in the control design for variable speed and torque drives are described. For the illustration of the method emulation of the pump mechanism is given.

A new method for calculating the resonant frequency of electrically thin and thick rectangular microstrip antennas, based on the fuzzy inference systems, is presented. The optimum design parameters of the fuzzy inference systems are determined by using the classical, modified, and improved tabu search algorithms. The calculated resonant frequency results are in very good agreement with the experimental results reported elsewhere.

Several techniques have been applied on leakage current waveforms in order to extract information regarding electrical activity on high-voltage insulators. However, a fully representative value is yet to be defined. In this article, a hybrid support vector fuzzy inference system is introduced as a classification tool. The system incorporates fuzzy logic, genetic algorithms, and support vector machines. Apart from the classification accuracy achieved, the system also produces a set of fuzzy rules under which the classification is made, allowing a further insight of the process. A comparison is made to other classification tools previously applied on the same data set.