Fuzzy Method Research Articles

Assessing digital transformation using fuzzy cognitive mapping supported by artificial intelligence techniques

The digital transformation process aims to align people, machines, and technologies in interactive systems, prompting businesses to adapt their models for competitiveness. Understanding and implementing digital transformation becomes crucial for organizations to integrate physical and virtual systems throughout their operations. The study presents a comprehensive model emphasizing intelligent technology, organization, and manufacturing as essential criteria. Integrating fuzzy cognitive maps and fuzzy DEMATEL methods, the study captures complex relationships and employs a dynamic analysis involving three firms in the defense industry. Results showcase the companies' diverse digital transformation levels, assessed through scenarios and predictive analyses. Additionally, a sensitivity analysis underscores the impact of adjustments to the top three criteria, aiding businesses in deciding their transformational starting points. This integrated method, underpinned by fuzzy cognitive maps and artificial intelligence, emerges as a decision support system and a roadmap for enterprises navigating digital transformation.

Evaluation of Earthquake Performance of Reinforced Concrete Buildings with Fuzzy Logic Method

Reinforced concrete building is a type of building whose structural system consists of reinforced concrete columns, beams, wall wall, slabs and foundations. Due to the strong earthquakes in our country, it has been observed that reinforced concrete buildings have been severely damaged or collapsed from past to present. The evaluation of buildings after earthquakes and their performances are quite important for the safety of life and property. In the literature, different methods have been developed for post-earthquake evaluation of buildings, either low-cost and fast or slow and high-cost and tool-demanding. In this study, in order to overcome the gap in the literature, the evaluation of earthquake performance of buildings with fuzzy logic method is discussed. In this context, the performance of the building was evaluated by taking into account the concrete compressive strength, number of floors, ground floor area, area of column and shear walls, ground floor area and architectural parameters. The data of 18, 28 and 146 buildings affected by the earthquakes in Afyon, Bingöl and Van provinces in 2002, 2003 and 2011, respectively, were used in the study. Out of the total 192 building data, 94 buildings were processed as data and fuzzy logic rules were applied. The remaining 98 buildings were tested with this method. The buildings considered are light, moderate and severely damaged or collapsed. Matlab program was used in the study. The method and result used in the study are in the class of fast and second stage methods in the literature, which are reliable but do not reflect the final result. With the method revealed in the study, the damage status of reinforced concrete buildings can be determined quickly and reliably. In this context, the buildings were evaluated as no damage/slight, moderate and severely damaged or collapsed. According to the results, the correct estimation rate of the damage status of 98 buildings was found to be 88%. It is seen that the fuzzy logic method can predict building performances at a quite high rate. Compared to the existing second stage assessment methods in the literature, this result is more conservative.

A navigational risk evaluation of ferry transport: Continuous risk management matrix based on fuzzy Best-Worst Method.

Ferry transport has witnessed numerous fatal accidents due to unsafe navigation; thus, it is of paramount importance to mitigate risks and enhance safety measures in ferry navigation. This paper aims to evaluate the navigational risk of ferry transport by a continuous risk management matrix (CRMM) based on the fuzzy Best-Worst Method (BMW). Its originalities include developing CRMM to figure out the risk level of risk factors (RFs) for ferry transport and adopting fuzzy BWM to estimate the probability and severity weights vector of RFs. Empirical results show that twenty RFs for ferry navigation are divided into four zones corresponding to their risk values, including extreme-risk, high-risk, medium-risk, and low-risk areas. Particularly, results identify three extreme-risk RFs: inadequate evacuation and emergency response features, marine traffic congestion, and insufficient training on navigational regulations. The proposed research model can provide a methodological reference to the pertinent studies regarding risk management and multiple-criteria decision analysis (MCDA).

Research on the Application Maturity of Enterprises’ Artificial Intelligence Technology Based on the Fuzzy Evaluation Method and Analytic Network Process

The aim of this study was to study the impact of artificial intelligence (AI) on enterprises in terms of strategy, technology, business operations, and organizational management. This study used grounded theory analysis to identify the influencing factors of AI technology application maturity in Chinese enterprises. Taking Chinese film and television enterprises as an example, this study constructed an AI technology application maturity evaluation index system for enterprises based on the analytic network process (ANP) and evaluated the application maturity of AI technology in enterprises in terms of enterprise strategy, technology, business operations, and organizational management. To comprehensively evaluate and empirically analyze the application maturity of enterprise AI technology, this study calculated the index weight based on the ANP, and combined it with the fuzzy comprehensive evaluation method to construct a comprehensive evaluation model. The research results showed that intelligence strategy was the element that was believed to be most affected by the maturity of enterprise AI technology. For technology, intelligence technology and equipment were the elements that were believed to be affected the most. For business operations, smart shooting was the element that was believed to be affected the most. With respect to organizational management, corporate culture was the element that was believed to be most affected. The results showed that the proposed methods for evaluating the application maturity of enterprise AI technology are scientific and effective. The results of this study provide a reference for promoting the application of AI, implementing the intelligence transformation, and enhancing the core competitiveness of enterprises.

Framework for Regional to Global Extension of Optical Water Types for Remote Sensing of Optically Complex Transitional Water Bodies

Water quality indicator algorithms often separate marine and freshwater systems, introducing artificial boundaries and artifacts in the freshwater to ocean continuum. Building upon the Ocean Colour- (OC) and Lakes Climate Change Initiative (CCI) projects, we propose an improved tool to assess the interactions across river–sea transition zones. Fuzzy clustering methods are used to generate optical water types (OWT) representing spectrally distinct water reflectance classes, occurring within a given region and period (here 2016–2021), which are then utilized to assign membership values to every OWT class for each pixel and seamlessly blend optimal in-water algorithms across the region. This allows a more flexible representation of water provinces across transition zones than classic hard clustering techniques. Improvements deal with expanded sensor spectral band-sets, such as Sentinel-3 OLCI, and increased spatial resolution with Sentinel-2 MSI high-resolution data. Regional clustering was found to be necessary to capture site-specific characteristics, and a method was developed to compare and merge regional cluster sets into a pan-regional representative OWT set. Fuzzy clustering OWT timeseries data allow unique insights into optical regime changes within a lagoon, estuary, or delta system, and can be used as a basis to improve WQ algorithm performance.

Vulnerability analysis of socio-ecological systems on small islands from the perspective of fisheries and tourism: A case study of Zhoushan Gouqi Island

Small island social-ecological systems are increasingly vulnerable to the combined impacts of human activities and environmental changes, making focused vulnerability analyses critical for sustainable governance. In this study, Gouqi Island, a remote island, was selected as the research site to establish a comprehensive vulnerability assessment framework based on the Vulnerability-Sensitivity-Danger (VSD) model. To account for potential interdependencies among indicators, a fuzzy integration method was employed to quantitatively assess vulnerability trends from 2016 to 2022. The analysis revealed that vulnerability reached its lowest point in 2018, peaked at 0.876 in 2020, and remained above 2016 levels through 2022, despite a slight decline. Additionally, machine learning techniques were used to identify key factors influencing vulnerability, highlighting indicators such as D24 (proportion of fisheries and tourism output), D41 (number of tourists), and D52 (aquaculture production) as particularly significant, though distinct from traditional sensitivity indicators. These findings inform the identification of critical factors affecting the island's sustainable development and lead to the proposal of adaptive governance strategies. The insights from this study provide valuable guidance for promoting sustainable development in similar remote islands.

Optical and Electronic Expert System for Diagnosing Eye Pathology in Glaucoma

For the first time, mathematical models for evaluating biomedical images were obtained using fuzzy set methods on the basis of expert knowledge bases, thus making it possible to carry out complex qualitative diagnostics and increase the reliability and efficiency of diagnosis. A methodology for analyzing biomedical images was formed based on the fuzzy set apparatus, which allowed a fuller assessment of the level of disease for glaucoma patients. Aspects of the practical implementation of the optical–electronic system for biomedical information processing were considered. An algorithm and an optical–electronic system for biomedical image analysis are proposed, and they can be used to increase the informativeness and reliability in the diagnosis of eye pathologies and glaucoma in particular.

Fuzzy anomaly scores for Isolation Forest

The detection of anomalies in data presents a significant challenge in various applications. The Isolation Forest (IF) has gained attention due to its notable performance features, including high accuracy, efficiency, simplicity, and rapid computation. However, prior research has primarily concentrated on the constructing isolation trees (iTrees), overlooking the considerable influence of anomaly scoring methods on anomaly detection performance. This study introduces an innovative anomaly scoring method that integrates fuzzy concepts to enhance detection performance. Fuzzy concepts adeptly manage ambiguity and uncertainty, making them more readily applicable in anomaly scoring than in iTree training. Unlike conventional methods that assign a sample to a single child node in the decision path, the proposed fuzzy anomaly scoring method allows samples to be assigned to all child nodes with varying membership degrees based on the target sample. Consequently, this method aggregates the path lengths of all external nodes in a weighted manner, minimizing the impact of irrelevant splits on anomaly scores. Considering that even IF algorithms using informative splits select splits based on data distribution rather than label information, introducing fuzziness to the splits themselves can effectively mitigate performance degradation caused by irrelevant splits. Extensive experiments on 25 benchmark datasets demonstrated that the proposed anomaly scoring method significantly improved both the performance and stability of anomaly detection with the base IF algorithm, outperforming other IF algorithms and fuzzy rough set-based anomaly detection methods.

A new system for underwater vehicle balancing control based on weightless neural network and fuzzy logic methods

<p>The utilization of humans to be in the water for short time, resulting in limited area underwater that can be explored, so the information obtained is very limited, plus the influence of irregular water movements, changes in waves, and changes in water pressure, indirectly also constitutes obstacle to this problem. One of the best solutions is to develop underwater vessel that can travel either autonomously or by giving control of movement and navigation systems. New system for underwater vehicle balance control through weightless neural network (WNN) and fuzzy logic methods was proposed in this study. The aim was to simplify complicated data source on stability system using WNN algorithm and determine depth level of autonomous underwater vehicle (AUV) through fuzzy logic method. Moreover, speed control of underwater vehicle was determined using fuzzy rule-based design and inference. The tests were conducted by showing convergence performance of system in the form of AUV simulator. The results showed that proposed system could produce real-time motion balance performance, faster execution time, and good level of accuracy. This study was expected to produce real-time motion balance system with better performance, faster execution time, and good level of accuracy which could be subsequently used to design simple, cheap, and efficient hardware prototype.</p>

Advanced digital competency assessment of vocational teachers': A new approach based on fuzzy-analytical hierarcy process

<p>Teachers need digital competence to adapt easily to the current digital era. This study tries to discover the perceptions of vocational high school (VHS) teachers in Indonesia related to advanced digital competencies, which include information, communication, content creation, digital security, and problem-solving competencies. The multi-criteria Analytical Hierarchy Process (AHP) problem-solving method is used to rank the priority digital competencies that are most mastered by the respondents, and then their performance is validated by the fuzzy AHP artificial intelligence-based method. A poll was conducted with 392 respondents, with the research instrument adopting the digital competency measurement platform from DigComp. The study's results show that the fuzzy AHP method has proven that the classical AHP method is a very good way to prioritize VHS teachers' digital skills based on several factors. The two methods gave almost identical results in determining the priority order of VHS teacher digital competencies. The survey results reveal that VHS teachers in Indonesia must immediately develop their skills in terms of digital content creation and digital security. Teachers, teacher professional organizations, and decision-makers are expected to use the findings of this study as a reference in implementing VHS teacher digital competency improvement trainings.</p>

Multi-objective predictive maintenance scheduling models integrating remaining useful life prediction and maintenance decisions

This paper presents two novel data-driven multi-objective predictive maintenance scheduling models that integrate remaining useful life (RUL) prediction into maintenance planning. First, we propose a deep learning ensemble model that includes a convolutional neural network and bidirectional long short-term memory network with a temporal self-attentional mechanism and Bayesian optimization method to predict system RUL effectively. Then, two novel multi-objective mixed-integer linear programming (MILP) models are developed based on the system-predicted RUL to deal with the system predictive maintenance problem, which aims to minimize the total maintenance completion time and maintenance-related costs simultaneously. To solve this problem, we design an iteration ϵ-constraint method to achieve Pareto solutions. Meanwhile, a fuzzy logic method is proposed to recommend a preferred Pareto solution for decision-makers. Finally, the aircraft engine C-MAPSS dataset from NASA validates that the effectiveness of the proposed data-driven multi-objective predictive maintenance scheduling models are effective. For the predictive maintenance of 20 aircraft engines, both the maintenance completion time and maintenance cost are reduced by more than 40%.

Multi-objective optimization of series-parallel system with mixed subsystems failure dependencies using NSGA-II and MOHH

In complex systems, failure dependencies play a crucial role in determining their overall performance. This paper explores the multi-objective optimization of series-parallel systems with mixed failure dependencies. By optimizing system cost and availability, the study aims to identify the most efficient redundancy and repair strategies. Two optimization algorithms, the non-dominated sorting genetic algorithm II (NSGA-II) and a novel multi-objective algorithm named the multi-objective hoopoe heuristic (MOHH), are utilized alongside constraint handling techniques to produce Pareto fronts. These fronts illustrate the trade-offs between cost and availability. Additionally, a fuzzy decision method is utilized to determine the best compromise solutions from each optimization technique. Comparing the results, NSGA-II consistently outperforms MOHH in providing better compromise solutions across five independent runs. However, MOHH demonstrates a better standard deviation in its performance.

Fuzzy Adaptive Knowledge-Based Inference Neural Networks: Design and Analysis.

A novel fuzzy adaptive knowledge-based inference neural network (FAKINN) is proposed in this study. Conventional fuzzy cluster-based neural networks (FCBNNs) suffer from the challenge of a direct extraction of fuzzy rules that can capture and represent the interclass heterogeneity and intraclass homogeneity when the data possess complex structures. Moreover, the capability of the cluster-based rule generator in FCBNNs may decrease with the increase of data dimensionality. These drawbacks impede the generation of desired fuzzy rules, and affect the inference results depending on the fuzzy rules, thereby limiting their generalization ability. To address these drawbacks, an adaptive knowledge generator (AKG), consisting of the observation paradigm (OP) and clustering strategy (CS), is effectively designed to improve the generalization ability in FAKINN. The OP distills the characteristic information (CI) from data to highlight the homogeneity and heterogeneity of objects, and the CS, viz., the weighted condition-driven fuzzy clustering method (WCFCM), is proposed to summarize the CI to construct fuzzy rules. Moreover, the feedback between the OP and CS can control the dimensionality of CI, which endows FAKINN with the potential to tackle high-dimensional data. The main originality of the study focuses on the AKG and WCFCM that are proposed to develop the structural design methodology of FNNs. The performance of FAKINN is evaluated on various benchmarks with 27 comparative methods, and two real-world problems are adopted to validate its effectiveness. Experimental results show that FAKINN outperforms the comparison methods.

Robust Adaptive Fuzzy Control for Second-Order Euler-Lagrange Systems With Uncertainties and Disturbances via Nonlinear Negative-Imaginary Systems Theory.

Ensuring robust and precise tracking control in the presence of uncertain multi-input-multi-output (MIMO) system dynamics and environmental variations is a significant challenge in the field of robust and adaptive control theory. While fuzzy control strategies have demonstrated good tracking performance in normal conditions, designing and tuning fuzzy controllers can be a challenging task in highly uncertain environments. In this study, we investigate a novel approach that combines robust nonlinear negative-imaginary (NI) systems theory with a self-adaptive fuzzy control scheme and the Lyapunov synthesis to develop a robust adaptive negative-imaginary-fuzzy (RANIF) control scheme. We optimize the critical parameters of the proposed fuzzy system using a self-tuning technique with a proportional-derivative sliding manifold. Furthermore, unlike the existing adaptive fuzzy control methods, we propose a small number of membership functions and systematically derive the fuzzy rules by employing Lyapunov, nonlinear NI, and dissipativity theories, which simplify the tuning process, work out the matter of "explosion of complexity," and reduce computational complexity. We demonstrate the global stability of the closed-loop system using nonlinear NI theory. To evaluate the effectiveness of our proposed approach, we present simulation results for two examples involving uncertain MIMO second-order Euler-Lagrange systems. These systems, known for their capacity to represent a diverse range of practical physical systems, serve as suitable testbeds for our methodology. Our results show that RANIF outperforms other control methods, such as nonlinear strictly NI-Fuzzy, fuzzy-logic control, model predictive control, and conventional PID control, in terms of robustness to disturbances and inestimable faults, trajectory tracking performance, and computational complexity.

A Two Phases Multiobjective Trajectory Optimization Scheme for Multi-UGVs in the Sight of the First Aid Scenario.

Timely delivery of first aid supplies is significant to saving lives when an accident happens. Among the promising solutions provided for such scenarios, the application of unmanned vehicles has attracted ever more attention. However, such scenarios are often very complex, while the existing studies have not fully addressed the trajectory optimization problem of multiple unmanned ground vehicles (multi-UGVs) against the scenario. This study focuses on multi-UGVs trajectory optimization in the sight of first aid supply delivery tasks in mass accidents. A two-stage completely decoupling fuzzy multiobjective optimization strategy is designed. On the first stage, with the proposed timescale involved tridimensional tunneled collision-free trajectory (TITTCT) algorithm, collision-free coarse tunnels are build within a tridimensional coordinate system, respectively, for the UGVs as the corresponding configuration space for a further multiobjective optimization. On the second stage, a fuzzy multiobjective transcription method is designed to solve the decoupled optimal control problem (OCP) within the configuration space with the consideration of priority constrains. Following the two-stage design, the computational time is significantly reduced when achieving an optimal solution of the multi-UGV trajectory planning, which is crucial in a first aid task. In addition, other objectives are optimized with the aspiration level reflected. Simulation studies and experiments have been curried out to testify the effectiveness and the improved computational performance of the proposed design.

Evaluating the Stages of Environmental Pollution and Vital Indicators in the Qayyarah Refinery Area, Mosul, Iraq

Oil spills can have varying degrees of impact on the aquatic environment depending on factors such as the type of oil spilled, the volume released, and the ecosystem affected because crude oil and refined petroleum contain harmful substances such as hydrocarbons, heavy metals, and toxic chemicals. When released into the water, these substances can have immediate and long-term effects on marine life. This research aims to find the factors affecting the degree of pollution from oil spills on the aquatic environment and the areas adjacent to the Qayyarah refinery in northern Iraq. Combines the fuzzy comprehensive evaluation method and the analytical hierarchy process to evaluate the degree of pollution from oil spill incidents in the areas adjacent to the refinery. The statistical analysis showed that there were statistically significant differences and that the value of the correlation coefficient was positive between exchanged cadmium, residual cadmium, exchanged lead, where the lead exchange rate ranged correlation coefficient at a minimum R² 0.674 and a clear increase in the number of bacteria indicating contamination the total number of bacteria coli, fecal coliform bacteria, and fecal strep bacteria (where the numbers of coliform bacteria ranged 102 × 102- (cells/011 mL, and fecal coliform bacteria were between 160 × 102 cells/011 mL. These rates are environmental and vital indicators of the presence of significant levels of organic pollution and evidence of the presence of microorganisms dangerous to the health of residents and living organisms.

Research on the Influence of Firm Digital Intelligence Transformation and Management Innovation on Performance and Sustainable Development: Empirical Evidence from China

From the perspective of firm capability, this study explores the impact of digital intelligence transformation and management innovation on the performance and sustainable development of Chinese firms. On the basis of constructing an evaluation index system of firm digital intelligence transformation and management innovation, a comprehensive evaluation model of firm digital intelligence transformation and management innovation is constructed via the analytic network process (ANP) and fuzzy comprehensive evaluation method. Taking Chinese film and television firms as an example, this study selects 160 data samples from 85 firms and constructs a structural equation model to empirically analyze the impact of digital intelligence transformation and management innovation on the performance of firms. The study results show that, in the context of digitalization and intelligence, firm digital intelligence capability and dynamic capability have a significant positive effect on firm performance. Firm digital intelligence transformation and management innovation directly impact performance and can play a role through firm digital intelligence capability and dynamic capability as intermediaries, and the chain intermediary effect of digital intelligence capability and dynamic capability is significant. The research results show that firm digital intelligence and dynamic capabilities provide a functional path for firm digital intelligence transformation and management innovation to improve performance. Moreover, the innovative application of digital intelligence technology has an important impact on the sustainable development of firms. Digital transformation and management innovation can help firms enhance their ability to manage environmental uncertainties, improve firm performance and foster the achievement of sustainable development goals.

New energy urban rail vehicle hybrid energy storage control system

Abstract With the increasing demand for urban transportation and the gradual strengthening of environmental protection awareness, new energy urban rail transportation has gradually become the future trend of urban development. The hybrid energy storage system is one of the key technologies to achieve the goals of energy saving and emission reduction of new urban rail vehicles, improving operation efficiency and comfort. In the traction stage of urban rail vehicles, the energy storage unit composed of lithium batteries and supercapacitors is jointly supplied with the power grid. In the braking stage, the energy storage unit composed of lithium batteries and supercapacitors is a common way for urban rail vehicles to recover energy. This study validates the efficacy of employing fuzzy control methodology to enhance the performance of the energy storage system. The experimental results show that the fuzzy control method can better balance the load distribution between lithium batteries and supercapacitors, thus extending their service life and improving the overall system performance. Based on the SOC energy management strategy of supercapacitors, urban rail vehicle power is allocated to supercapacitors and lithium-ion batteries via a low-pass filter. System power is adjusted based on supercapacitor SOC, determining capacitor charging/discharging states and suppressing system output power.

Design of small independent photovoltaic power generation system

Abstract This article designs a small independent photovoltaic power generation system, which includes solar panels, controllers, batteries, and inverter modules. The design requirements and principles of boost converters and inverters were elaborated, and the inverters were simulated in Matlab; The fuzzy control method was selected to track the maximum power point, and its effectiveness was verified through MATLAB simulation; Corresponding methods have been proposed to prevent or reduce factors that affect the output power of photovoltaic arrays, such as power attenuation, shielding loss, and temperature rise loss. It has extremely important significance for promoting the development of photovoltaic power generation, achieving zero carbon goals, and sustainable development of human society.

Comparison of the Performance of Type-1 and Interval Type-2 Fuzzy PI Controllers for Liquid Level Control in a Coupled Tank System

In this study, liquid level control in a coupled tank system is realized with PI controllers whose parameters are adapted using fuzzy logic approximation methods. Type-1 fuzzy PI control, interval type-2 fuzzy PI control and classical PI+Feedforward control methods are applied to the tank system in real time for different reference liquid levels. The performances of the controllers are compared in terms of system response parameters such as rise time, settling time and overshoot percentage. In addition, the performance evaluation of the controllers over a certain period of time is also compared by calculating integral-based concepts that express error performance metrics such as integral square error (ISE), integral time square error (ITSE), integral absolute error (IAE) and integral time absolute error (ITAE). The obtained results show that the type-2 fuzzy PI controller shows the best performance, followed by type-1 fuzzy PI control and classical PI+FF controller, respectively.