Artificial Intelligence and Fuzzy Systems

Abstract submission deadline

closed (31 August 2023)

Manuscript submission deadline

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Topic Information

Dear Colleagues,

Artificial intelligence (AI) includes the theory and development of computer systems that are able to perform tasks that require human intelligence, such as problem solving, reasoning, planning, natural language understanding, computer vision, automatic programming, and machine learning, among others. It is increasingly present today. The large amount of information that is currently stored requires specialized techniques for automation, precision, and speed in data analysis. More and more organizations are interested in applying it to improve the effectiveness and efficiency of their business processes.

Indeed, fuzzy systems cover all aspects of the theory and applications of fuzzy sets and systems in addition to their hybridizations with other artificial and computational intelligence techniques. These techniques are becoming more and more relevant because they allow a transparent description of knowledge in terms of linguistic rules. In many real-world applications, fuzzy systems realize precise systems that introduce high interpretability.

This Topic, “Artificial Intelligence and Fuzzy Systems”, invites papers on theoretical and applied issues, including, but not limited to, the following:

• Big data, data engineering, and data analytics.
• Artificial intelligence, machine learning, and cognitive computing.  
• Data mining, information retrieval, and business intelligence.
• Artificial intelligence in business, robotics, healthcare, or multimedia technology.
• Artificial neural networks and deep learning.   
• Ethics of artificial intelligence.
• Explainable artificial intelligence.
• Interpretable and interactive approaches to uncertainty in AI.
• Fuzzy databases and informational retrieval.
• Theory and applications of fuzzy logic and probabilistic approaches.
• Fuzzy decision analysis, multicriteria decision making and decision support.
• Interdisciplinary field of fuzzy logic and data mining.

This Topic will present the results of research describing recent advances in both the artificial intelligence and the fuzzy systems.

Prof. Dr. Amelia Zafra
Prof. Dr. Jose Manuel Soto Hidalgo
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	-	-	2020	20.8 Days	CHF 1600
Algorithms algorithms	2.3	3.7	2008	15 Days	CHF 1600
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400
Big Data and Cognitive Computing BDCC	3.7	4.9	2017	18.2 Days	CHF 1800
Machine Learning and Knowledge Extraction make	3.9	8.5	2019	19.9 Days	CHF 1800
Sensors sensors	3.9	6.8	2001	17 Days	CHF 2600

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Published Papers (6 papers)

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All AI Algorithms Applied Sciences BDCC MAKE Sensors

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20 pages, 6710 KiB  

Open AccessArticle

Fault-Tolerant Control for Carrier-Based Aircraft Based on Adaptive Fuzzy Sliding-Mode Method

by Zhenlin Xing and Jianliang Ai

Appl. Sci. 2023, 13(23), 12685; https://doi.org/10.3390/app132312685 - 26 Nov 2023

Viewed by 723

Abstract

Carrier-based aircraft landing involves complex system engineering characterised by strong nonlinearity, significant coupling and susceptibility to environmental disturbances, and autonomous landing of carrier-based aircraft under fault states is even more challenging and riskier. To address the control-system problems of loss of efficiency and [...] Read more.

Carrier-based aircraft landing involves complex system engineering characterised by strong nonlinearity, significant coupling and susceptibility to environmental disturbances, and autonomous landing of carrier-based aircraft under fault states is even more challenging and riskier. To address the control-system problems of loss of efficiency and performance due to actuator faults and performance degradation due to various unknown disturbances, presented here is fault-tolerant control for carrier-based aircraft based on adaptive fuzzy sliding-mode fault-tolerant control (AFSMFTC). First, three models are built (the carrier-based aircraft fault model, the carrier air wake model and the deck motion model), and the control framework of the autonomous landing control system is introduced. Next, a longitudinal and lateral flight channel controller comprising an adaptive fuzzy network, adaptive laws and a sliding-mode controller is designed using the AFSMFTC method. The adaptive fuzzy network implements fuzzy approximation for the sliding-mode switching terms to further offset errors induced by unknown disturbances, the adaptive laws compensate for actuator faults, and the sliding-mode controller ensures tracking of the overall flight path. Furthermore, the stability of the fault-tolerant method is demonstrated using the Lyapunov function. Finally, simulation and comparative experiments show that the proposed fault-tolerant method has outstanding control performance and strong fault-tolerant capability, thereby providing an effective and feasible solution for designing an autonomous landing system for carrier-based aircraft under fault states. Full article

(This article belongs to the Topic Artificial Intelligence and Fuzzy Systems)

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19 pages, 7647 KiB  

Open AccessArticle

Battery Charge Control in Solar Photovoltaic Systems Based on Fuzzy Logic and Jellyfish Optimization Algorithm

by Ramadan Ahmed Ali Agoub, Aybaba Hançerlioğullari, Javad Rahebi and Jose Manuel Lopez-Guede

Appl. Sci. 2023, 13(20), 11409; https://doi.org/10.3390/app132011409 - 18 Oct 2023

Cited by 3 | Viewed by 954

Abstract

The study focuses on the integration of a fuzzy logic-based Maximum Power Point Tracking (MPPT) system, an optimized proportional Integral-based voltage controller, and the Jellyfish Optimization Algorithm into a solar PV battery setup. This integrated approach aims to enhance energy harvesting efficiency under [...] Read more.

The study focuses on the integration of a fuzzy logic-based Maximum Power Point Tracking (MPPT) system, an optimized proportional Integral-based voltage controller, and the Jellyfish Optimization Algorithm into a solar PV battery setup. This integrated approach aims to enhance energy harvesting efficiency under varying environmental conditions. The study’s innovation lies in effectively addressing challenges posed by diverse environmental factors and loads. The utilization of MATLAB 2022a Simulink for modeling and the Jellyfish Optimization Algorithm for PI-controller tuning further strengthens our findings. Testing scenarios, including constant and variable irradiation, underscore the significant enhancements achieved through the integration of fuzzy MPPT and the Jellyfish Optimization Algorithm with the PI-based voltage controller. These enhancements encompass improved power extraction, optimized voltage regulation, swift settling times, and overall efficiency gains. Full article

(This article belongs to the Topic Artificial Intelligence and Fuzzy Systems)

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24 pages, 3010 KiB  

Open AccessArticle

A Fuzzy Model for Reasoning and Predicting Student’s Academic Performance

by Mohamed O. Hegazi, Bandar Almaslukh and Khadra Siddig

Appl. Sci. 2023, 13(8), 5140; https://doi.org/10.3390/app13085140 - 20 Apr 2023

Cited by 5 | Viewed by 2157

Abstract

Evaluating students’ academic performance is crucial for assessing the quality of education and educational strategies. However, it can be challenging to predict and evaluate academic performance under uncertain and imprecise conditions. To address this issue, many research works have employed fuzzy concepts to [...] Read more.

Evaluating students’ academic performance is crucial for assessing the quality of education and educational strategies. However, it can be challenging to predict and evaluate academic performance under uncertain and imprecise conditions. To address this issue, many research works have employed fuzzy concepts to analyze, predict, and make decisions about students’ academic performance. This paper investigates the use of fuzzy concepts in research related to evaluating, analyzing, predicting, or making decisions about student academic performance. The paper proposes a fuzzy model, called FPM (Fuzzy Propositional Model), for reasoning and predicting students’ academic performance. FPM aims to address the limitations of previous studies by incorporating propositional logic with fuzzy sets concept, which allows for the representation of uncertainty and imprecision in the data. FPM integrates and transforms if-then rules into weighted fuzzy production rules to predict and evaluate academic performance. This paper tests and evaluates the FPM in two scenarios. In the first scenario, the model predicts and examines the impact of absenteeism on academic performance where there is no clear relation between the two parts of the dataset. In the second scenario, the model predicts the final exam results using the lab exam results, where the data are more related. The FPM provides good results in both scenarios, demonstrating its effectiveness in predicting and evaluating students’ academic performance. A comparison study of the FPM’s results with a linear regression model and previous work showed that the FPM performs better in predicting academic performance and provides more insights into the underlying factors affecting it. Therefore, the FPM could be useful in educational institutions to predict and evaluate students’ academic performance, identify underlying factors affecting it, and improve educational strategies. Full article

(This article belongs to the Topic Artificial Intelligence and Fuzzy Systems)

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20 pages, 30113 KiB  

Open AccessArticle

Multi-Boundary Empirical Path Loss Model for 433 MHz WSN in Agriculture Areas Using Fuzzy Linear Regression

by Supachai Phaiboon and Pisit Phokharatkul

Sensors 2023, 23(7), 3525; https://doi.org/10.3390/s23073525 - 28 Mar 2023

Cited by 3 | Viewed by 1235

Abstract

Path loss models are essential tools for estimating expected large-scale signal fading in a specific propagation environment during wireless sensor network (WSN) design and optimization. However, variations in the environment may result in prediction errors due to uncertainty caused by vegetation growth, random [...] Read more.

Path loss models are essential tools for estimating expected large-scale signal fading in a specific propagation environment during wireless sensor network (WSN) design and optimization. However, variations in the environment may result in prediction errors due to uncertainty caused by vegetation growth, random obstruction or climate change. This study explores the capability of multi-boundary fuzzy linear regression (MBFLR) to establish uncertainty relationships between related variables for path loss predictions of WSN in agricultural farming. Measurement campaigns along various routes in an agricultural area are conducted to obtain terrain profile data and path losses of radio signals transmitted at 433 MHz. Proposed models are fitted using measured data with “initial membership level” (${\mu }_{AI}$). The boundaries are extended to cover the uncertainty of the received signal strength indicator (RSSI) and distance relationship. The uncertainty not captured in normal measurement datasets between transmitter and receiving nodes (e.g., tall grass, weed, and moving humans and/or animals) may cause low-quality signal or disconnectivity. The results show the possibility of RSSI data in MBFLR supported at an ${\mu }_{AI}$ of 0.4 with root mean square error (RMSE) of 0.8, 1.2, and 2.6 for short grass, tall grass, and people motion, respectively. Breakpoint optimization helps provide prediction accuracy when uncertainty occurs. The proposed model determines the suitable coverage for acceptable signal quality in all environmental situations. Full article

(This article belongs to the Topic Artificial Intelligence and Fuzzy Systems)

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23 pages, 4833 KiB  

Open AccessArticle

Modelling and Prediction of Reactive Power at Railway Stations Using Adaptive Neuro Fuzzy Inference Systems

by Manuela Panoiu, Caius Panoiu and Sergiu Mezinescu

Appl. Sci. 2023, 13(1), 212; https://doi.org/10.3390/app13010212 - 24 Dec 2022

Cited by 1 | Viewed by 1317

Abstract

Electricity has become an important concern in today’s society. This is due to the fact that the electric grid now has a greater number of non-linear components. The AC-powered locomotive is one of these non-linear components. The aim of this paper was to [...] Read more.

Electricity has become an important concern in today’s society. This is due to the fact that the electric grid now has a greater number of non-linear components. The AC-powered locomotive is one of these non-linear components. The aim of this paper was to model and predict the reactive power produced by an AC locomotive. This paper presents a study on the modelling and prediction of reactive power produced by AC-powered electric locomotives. Reactive power flow has a significant impact on network voltage levels and power efficiency. The research was conducted by using intelligent techniques—more precisely, by using the adaptive neuro fuzzy inference system (ANFIS). Several approaches to the ANFIS structure were used in the research. Of these, we mention the ANFIS-grid partition, ANFIS subtractive clustering and ANFIS fuzzy c-means (FCM) clustering. Thus; for modelling and predicting reactive power, ANFIS was trained, then tested. For the training of ANFIS, experimental data obtained from measurements performed in a train supply sub-station were used. The measurements were taken over a period of time when the locomotives were far away from the station, close to the station, and at the station, respectively. The currents and voltages from the supply substation, respectively the active, reactive, and distorted powers, were measured on the data acquisition board. With the measured data of the reactive power, the modelling with ANFIS was performed, and a prediction of the variation in the reactive power was made. The paper analysed the results of the modelling by comparing between several types of ANFIS architectures. The values of RMSE, RMS and the training time of ANFIS were compared for several structures of ANFIS. Full article

(This article belongs to the Topic Artificial Intelligence and Fuzzy Systems)

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18 pages, 3721 KiB  

Open AccessArticle

A Fuzzy Inference System for Detection of Positive Displacement Motor (PDM) Stalls during Coiled Tubing Operations

by Rafael Augusto Galo Fernandes, Paloma Maria Silva Rocha Rizol, Andreas Nascimento and José Alexandre Matelli

Appl. Sci. 2022, 12(19), 9883; https://doi.org/10.3390/app12199883 - 30 Sep 2022

Viewed by 1566

Abstract

Positive Displacement Motors (PDM) are extensively used in the oilfield, either in drilling or in coiled tubing (CT) operations. They provide a higher rate of penetration and the possibility of drilling horizontal wells. For coiled tubing operations, PDMs can mill through obstructions and [...] Read more.

Positive Displacement Motors (PDM) are extensively used in the oilfield, either in drilling or in coiled tubing (CT) operations. They provide a higher rate of penetration and the possibility of drilling horizontal wells. For coiled tubing operations, PDMs can mill through obstructions and enable shut-in wells to work again. One of the major challenges while using these tools is the motor stalling, which can lead to serious damage to the PDM and lost time events in drilling and workover rigs. These events result in total losses of hundreds of thousands of dollars, and their avoidance mostly depends on trained and fully aware equipment operators. If a PDM starts to stall, the pumping needs to be halted immediately or the tool may fail. This paper describes the use of a Fuzzy Inference System (FIS) to detect the stalling events as they start to happen using the acquisition data from the coiled tubing unit, the output of the FIS could then trigger an alarm for the operator to take the proper action or remotely stop the pump. The FIS was implemented in Python and tested with real CT milling acquisition data. When tested using real data, the system analyzed 68,458 acquisition points and detected 94% of the stalling events across this data during its first seconds, whereas, during the real job, a CT operator could take longer to notice this event and take the proper action, or even take no action. If the FIS was applied on a real coiled tubing acquisition system, it could reduce PDMs over-pressurization due to stalling, leading to an increase on its useful life and decrease on premature failure. As of now there is no similar system in the market or published and this kind of operation is fully performed using human supervision only. Full article

(This article belongs to the Topic Artificial Intelligence and Fuzzy Systems)

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