Fuzzy Logic Operations Research Articles

Data Hiding Scheme for Spatial Domain Images Using Fuzzy Logic and Modulus Operation

Data Hiding Scheme for Spatial Domain Images Using Fuzzy Logic and Modulus Operation

Site selection for biomass-solar hybrid renewable energy facilities: Spatial modelling based on fuzzy logic-geographic information systems

The aim of this study is to propose a fuzzy logic-geographic information systems (GIS) based spatial model within the context of hybrid renewable energy, rural areas, and spatial planning approach in the site selection process of renewable energy facilities. In this context, potential suitability index and site selection suitability index for biomass-solar hybrid renewable energy facilities are determined within the spatial model, through a case study conducted in Türkiye. The study involves a two-stage process: the first stage focuses on establishing the national-level potential, and the second stage involves site selection in areas with high potential. Suitable locations are determined based on the optimal suitability index values, which are derived from fuzzy logic operators including AND, OR, SUM, PRODUCT, and GAMMA, applied in the GIS. The study results indicate that by assigning different γ values to the fuzzy GAMMA operator, results for the fuzzy AND (γ = 0.45–0.60), fuzzy OR (γ = 1.00), fuzzy SUM (γ = 1.00), and fuzzy PRODUCT (γ = 0.00–0.15) operators can be obtained. Furthermore, as the γ value increases, the suitability index also rises, and the optimal site selection occurs at γ = 0.90. The performance of the fuzzy GAMMA operator in big data sets and two-stage processes demonstrates its applicability in site selection.

Understanding the invasion of the macroalga Rugulopteryx okamurae (Ochrophyta) in the northern Alboran Sea through the use of biogeographic models

Rugulopteryx okamurae is a brown alga native to the northwestern Pacific and invasive elsewhere. It was first sighted in the southern Strait of Gibraltar in 2015, expanded to the northern Strait of Gibraltar in 2016, and had covered most of the northern Alboran Sea by 2021. Understanding the factors that may explain its different phases of colonisation is crucial for developing mitigation and control measures. We modelled the yearly distribution of R. okamurae in the northern Alboran Sea from 2016 to 2021 using the favourability function, which produces commensurable biogeographic models despite variations in species prevalence across years. This function also allows the use of fuzzy logic operations to combine previous environmental models based on different explanatory factors, namely biotope, biocoenosis, coastal influence, accessibility through dispersion, and oceanographic characteristics. Significant environmental models were assembled, and their fuzzy intersection and union applied. This resulted in two final biogeographic models for each year, which were used to predict the distribution of the species in following years. The biogeographic models exhibited a high predictive capacity, as most years accurately predicted colonisation in the following year or even multiple years ahead. Accessibility through dispersion and oceanographic characteristics were critical during the initial years of establishment, while complete establishment depended on all five factors together. Expansion to the whole northern Alboran coast was explained by favourable conditions for any of the explanatory factors, while all factors except coastal influence explained the saturation of the invasion. We conclude that the biogeographic models prove valuable in understanding the factors that contributed to the spread of R. okamurae in the northern Alboran Sea. This approach could help prevent further colonisation and mitigate the ecosystem and commercial consequences of R. okamurae's invasion.

Comparative Study of Fuzzy Logic Operators

Fuzzy logic is currently very relevant because it offers a new way to approach tuning and decision-making problems. In this paper, we discuss a comparative study between Zadeh fuzzy operators and probabilistic operators, which are used during the activation outputs of the rules, to see which of them maximize or minimize the result of the defuzzification, in a fuzzy inference system used to create the fuzzy command control model, in artificial intelligence. The aim of this paper is to conduct a comparative study of rule output values by applying Zadeh's fuzzy operators and those of probability when activating rule outputs; and not to create other applications or to compare our solutions with previous results. We point out that we have carried out this comparative study of Zadeh fuzzy operators and probabilistic ones by exploiting several numerical examples such as in [1, 2, 3, 4, 5]. But to lighten the writing of this article, knowing that the steps of fuzzy inference are manually tedious, we consider the control data of a house fan, with two inputs (temperature and humidity) and one output (fan speed) processed by Baali Sabeur & Mahmoudi Messaoud in 2022. We have presented the cuts of the outputs from Zadeh's methods compared to those called probabilistic. After Fuzzification, activation of the Rules outputs, Aggregation of the outputs and defuzzification, we identified the fuzzy operators which maximize and those which minimize the net outputs, among the two families.

Stakeholder Analysis using Fuzzy Logic Operations for Integrated User Story Prioritisation Approach in Agile-Scrum Method

Effective prioritisation of user stories in Agile-Scrum projects is pivotal to ensuring optimal allocation of resources and meeting both user and system requirements. The process of prioritising user stories requires careful stakeholder analysis to determine their influence, the significance of their story, and the value it adds to the project. Common challenges, such as conflicting views, stakeholder exclusion, requirements overflow, insufficient stakeholder input, and incorrect prioritisation of user stories, are typical. This study presents an innovative approach to user story prioritisation by integrating stakeholder analysis as part of the user story prioritisation process in the Agile-Scrum method. The analysis employs fuzzy logic operations to rank stakeholders based on selected parameters, facilitating a more comprehensive understanding of their contributions and concerns. The study’s application focuses on the context of an ATM system’s requirements and associated user stories. Feedback was gathered from software experts well-versed in practising the Agile methodology within their respective organisations. Results from this study highlight the positive impact of stakeholder analysis during the prioritisation process. At the same time, the software experts praised the approach’s ability to integrate functional and non-functional user stories during the prioritisation process. By systematically addressing stakeholder perspectives, this approach ensures a balanced consideration of diverse user needs and system requirements, ultimately enhancing the accuracy and efficacy of user story prioritisation within Agile-Scrum projects.

Methods of accounting for measurement uncertainty and semantic uncertainty in the classification of the state of objects according to quality indicators

The paper proposes methods of accounting for measurement uncertainty and semantic uncertainty when consistently using measurement and classification procedures to classify the state of objects according to their quality indicators. Methods of accounting for uncertainty are distinguished by two approaches: the first one is based on accounting for measurement uncertainty when constructing a classification scale, and the second one is based on presenting the result of measuring a quality indicator as a fuzzy number, the carrier of which is the expanded measurement uncertainty. Using these methods, the state of the object can be classified according to one or more categories of the classification scale, indicating the degree of belonging to these categories. Recommendations for the application of two approaches to account for measurement uncertainty in the subsequent classification are given. The use of one or more fuzzy classification scales is recommended when classifying the state of an object by a single quality indicator or by several quality indicators that are combined into a group indicator after classification using the fuzzy logic operators, such as arithmetic mean, as well as pairwise volume operators. When using single and group quality indicators, measurement uncertainty may be accounted for when constructing a classification scale, that is, when using a fuzzy scale. At the same time, the components of the measurement uncertainty preceding the classification and the vagueness of the expert information that may be used in the classification can be accounted for. In this way, the following components of the uncertainty can be defined: instrumental uncertainty and semantic uncertainty. The use of a clear classification scale when presenting the measurement result as a fuzzy number is recommended when classifying the state of the object according to a complex quality indicator that is related to individual quality indicators by a certain dependence or by accounting for the weighting factors of individual quality indicators. It is shown that when accounting for the measurement uncertainty, the state of the object may be classified into one or several categories of the classification scale, indicating the degree of belonging to each category.

Optimal solutions to granular fuzzy relation equations with fuzzy logic operations

Fuzzy relation equations are commonly utilized to describe the fuzzy relationship between the antecedent and the consequent parts of complex data environment, and play a vital role in fuzzy system modeling. An interesting topic is to determine fuzzy relation equations based on existing fuzzy logic operations, including the t-norms and t-conorms (s-norms). However, fuzzy rule-based models developed in form of numeric values still face significant challenges in accuracy and interpretability. This study aims to introduce the idea of granular computing to formulate granular fuzzy relation equations based on the fuzzy logic operations and evaluate the performance of granular fuzzy relation equations using the general principle of justifiable granularity. A two-phase development of granular fuzzy relation equations is designed: In the first phase, fuzzy relation equations are constructed based on t-norms and t-conorms; in the second phase, granular augmentation of fuzzy relation equations is realized by allocating a certain level of information granularity as the granular parameters of fuzzy relations. The capability of granular outputs is evaluated with the aid of two conflicting criteria—coverage (cov) and specificity (sp). The optimal solutions to granular fuzzy relation equations are explored using combinations of optimization algorithms. The originality of this study lies on the development of granular fuzzy relation equations with fuzzy logic operations, which provides a human-centric platform of fuzzy system modeling to enhance accuracy and interpretability. Experimental studies are carried out based on the UCI and KEEL machine learning datasets to report on the feasibility and performance of the proposed model.

МАТЕМАТИЧНІ СИСТЕМИ ДЛЯ РЕАЛІЗАЦІЇ ШТУЧНИХ НЕЙРОННИХ МЕРЕЖ, ОРІЄНТОВАНИХ НА ХМАРОВІ ОБЧИСЛЕННЯ

The article provides a detailed overview of research focusing on artificial neural networks (ANNs) and their applications in cloud computing. Research methods of organizational development and changes based on artificial intelligence technologies and intellectual support systems are presented in the plane of: intellectual expert systems; inductive systems; semantic networks, neural networks, genetic algorithms. The aim of the study. The research is aimed at the study and analysis of modern mathematical systems used to implement artificial neural networks (ANNs). The main focus of the work is on how each artificial neuron in the network is characterized by its current state, which is similar to nerve cells in the brain that can be excited or inhibited. A detailed description of the functioning of neurons is provided, including the processes of summation of input signals and activation using activation functions. Special attention is paid to multilayer neural networks and their ability to form complex multidimensional functions. The methods of building decision-making models based on the analysis of unclear situations and reference states determined by experts are defined. The process of comparing the real states of organizations with reference ones for making optimal decisions is considered. The importance of fuzzy logical operations for determining the degree of closeness of various situations is described. Fuzzy reference situations for cloud computing and their impact on decision-making in various scenarios are proposed. Examples of real and hypothetical fuzzy situations are given, and methods of determining the fuzzy correspondence between different reference situations are also considered. The final part of the abstract emphasizes the possibilities and advantages of using such models in cloud computing, emphasizing their importance for the development of organizations and systems.

Covering variable precision fuzzy rough sets based on overlap functions and the application to multi-label classification

Fuzzy rough sets (FRSs) play a significant role in the field of data analysis, and one of the common methods for constructing FRSs is the use of the fuzzy logic operators. To further extend FRSs theory to more diverse information backgrounds, this article proposes a covering variable precision fuzzy rough set model based on overlap functions and fuzzy β-neighbourhood operators (OCVPFRS). Some necessary properties of OCVPFRS have also been studied in this work. Furthermore, multi-label classification is a prevalent task in the realm of machine learning. Each object (sample or instance) in multi-label data is associated with various labels (classes), and there are numerous features or attributes that need to be taken into account within the attribute space. To enhance various performance metrics in the multi-label classification task, attribute reduction is an essential pre-processing step. Therefore, according to overlap functions and fuzzy rough sets’ excellent work on applications: such as image processing and multi-criteria decision-making, we establish an attribute reduction method suitable for multi-label data based on OCVPFRS. Through a series of experiments and comparative analysis with existing multi-label attribute reduction methods, the effectiveness and superiority of the proposed method have been verified.

Improving compound fault diagnostic accuracy of drilling permanent magnet synchronous motor using information fusion

The diagnostic accuracy of compound faults is low due to the similarity of fault symptoms. Accurate diagnostic results can only be obtained by fusing all available information on compound faults. However, a single information fusion method cannot fuse all available information well because the characteristics of fault information are different. A method combining three kinds of information fusion technology is proposed to improve the compound fault diagnostic accuracy of the drilling permanent magnet synchronous motor (DPMSM). The fault symptoms are fused by Bayesian inference using the Bayesian Network. The additional information is fused by fuzzy logic operation using the Fuzzy Theory. The fusion results obtained from the above two methods are fused as evidence by mathematical logic using Evidence Theory to obtain the final diagnosis results. The effectiveness of the proposed method is verified by Cases. After fusion, “RoEc” fault in Case A rises to 0.933 from 0.826, “ItSc” fault in Case B rises to 0.524 from 0.262, and “RoEc” fault in Case C rises to 0.344 from 0.046 while “ItSc” down to 0.172 from 0.458. Cases indicate it can effectively improve the fault diagnostic accuracy and correct the results of missed diagnosis and misdiagnosis in compound faults of the DPMSM in drilling engineering. The effect of this method is influenced by the selection of specific experts and the accuracy of statistical data.

A fluorescence based dual sensor for Zn2+ and PO43- and the application of soft computing methods to predict machine learning outcomes.

A phenolphthalein-based Schiff base, 3,3-bis-{4-hydroxy-3-[(pyridine-2-ylmethylimino)-methyl]-phenyl}-3H-isobenzofuran-1-one (PAP), has been synthesized and used for selective fluorescence 'turn on' and 'turn off' sensing of Zn2+ and PO43- respectively. The limit of detection using the 3σ method for Zn2+ is found to be 19.3 nM and that for PO43- is 8.3 μM. The sensing mechanism of PAP for Zn2+ ions has been explained by 1H NMR, 13C NMR, TRPL, ESI-MS, FT-IR, and DFT based calculations. Taking advantage of this fluorescence 'on-off' behavior of PAP in the sequential presence of Zn2+ and PO43- a two input fuzzy logic (FL) operation has been constructed. The chemosensor PAP can thus act as a metal ion and anion responsive molecular switch, and its corresponding emission intensity is used to mimic numerous FL functions. To replace various expensive, time-consuming experimental procedures, we implemented machine learning soft computing tools, such as fuzzy-logic, artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS), to correlate as well as predict the fluorescence intensity in the presence of any equivalent ratio of Zn2+ and PO43-. The statistical performance measures (MSE and RMSE, for example) show that the projected values of the cation and anion sensing data by the ANFIS network are the best and closer to the experimental values.

Using fuzzy logic to compare species distribution models developed on the basis of expert knowledge and sampling records

BackgroundExperts use knowledge to infer the distribution of species based on fuzzy logical assumptions about the relationship between species and the environment. Thus, expert knowledge is amenable to fuzzy logic modelling, which give to propositions a continuous truth value between 0 and 1. In species distribution modelling, fuzzy logic may also be used to model, from a number of records, the degree to which conditions are favourable to the occurrence of a species. Therefore, fuzzy logic operations can be used to compare and combine models based on expert knowledge and species records. Here, we applied fuzzy logic modelling to the distribution of amphibians in Uruguay as inferred from expert knowledge and from observed records to infer favourable locations, with favourability being the commensurable unit for both kinds of data sources. We compared the results for threatened species, species considered by experts to be ubiquitous, and non-threatened, non-ubiquitous species. We calculated the fuzzy intersection of models based on both knowledge sources to obtain a unified prediction of favourable locations.ResultsModels based on expert knowledge involved a larger number of variables and were less affected by sampling bias. Models based on experts had the same overprediction rate for the three types of species, whereas models based on species records had a lower prediction rate for ubiquitous species. Models based on expert knowledge performed equally as well or better than corresponding models based on species records for threatened species, even when they had to discriminate and classify the same set of records used to build the models based on species records. For threatened species, expert models predicted more restrictive favourable territories than those predicted based on records. Observed records generated the best-fitted models for non-threatened non-ubiquitous species, and ubiquitous species.ConclusionsFuzzy modelling permitted the objective comparison of the potential of expert knowledge and incomplete distribution records to infer the territories favourable for different species. Distribution of threatened species was able to be better explained by subjective expert knowledge, while for generalist species models based on observed data were more accurate. These results have implications for the correct use of expert knowledge in conservation planning.

Remote sensing methods for striped marine oil spill detection in narrow ship channels

Maritime transport is the backbone of international trade and the global economy. With the increasing frequency of maritime transport, oil spills are on the rise and so is the risk of oil spills in shipping lanes. Therefore, timely oil spill monitoring at the ship channel is of far-reaching significance for marine disaster prevention and control. This study selects oil spill in the ship channel of the northern Yellow Sea as an example and proposes an object-oriented fuzzy logic classification method combining selected multi-feature parameters based on Sentinel-2 data. Firstly, sensitive bands for oil spills are selected to perform multi-scale segmentation of the oil spill object. Then, 30 samples of oil spill objects were uniformly selected in the oil spill area and the average fuzzy membership values were calculated using 11 membership functions by combining the preferred spectral, geometric, texture, and custom features. Finally, five fuzzy logic operators are used to achieve the final classification, while the oil spill misclassification information is processed and compared with the pixel-based SVM and RF algorithms for accuracy. The experimental results show that the oil spill detection based on the “AND” operator is the best compared to other fuzzy operators, with an overall accuracy of 85.19% and a KAPPA coefficient of 0.753. Relative to the pixel-based Support Vector Machine (SVM) and Random Forest (RF) algorithms, the overall accuracy was improved by 15.66% and 11.25%, respectively, and the KAPPA coefficients were improved by 0.072 and 0.161, respectively. The method is feasible for oil spill detection at ship channels and provides technical support for future marine oil spill detection work.

Probabilistic Detection of Indoor Events Using a Wireless Sensor Network-Based Mechanism.

Wireless sensor networks (WSNs) have been commonly utilized in event detection and environmental observation applications. The main aim of event detection is to define the presence or absence of an event. Various existing studies in the field of event detection depend on static or threshold values to reveal the occurrence of an event, which can result in imprecise sensor readings. Recently, many studies have utilized fuzzy logic to treat fluctuating sensor readings; as a result, they have decreased the number of false alarms created. However, there is some attention required when utilizing fuzzy logic. One aspect is that the efficiency and accuracy of the fuzzy membership function can be impacted by the utilization of heterogeneous sensors, which may increase the complexity of the fuzzy logic operation as the number of inputs rises. To address these issues, this paper proposes an approach named Probabilistic Collaborative Event Detection (PCED), which is a hybrid event detection technique that is based on a cluster WSN topology. The PCED approach utilizes a validated probabilistic technique for heterogeneous sensor nodes to transform sensing values into probability formulas and introduces a Cluster Head Decision Mechanism to make decisions based on the aggregated data from the sensors. The proposed approach employs fuzzy logic at the fusion center level to enhance the precision of event detection. The effectiveness of this method is thoroughly evaluated using MATLAB software, demonstrating an improvement in the probability of detection and a decrease in the probability of false alarms. PCED is compared to well-established event detection mechanisms such as the REFD mechanism. The results show that PCED reduces the occurrence of false alarms from 37 to 3 in certain scenarios, while improving detection accuracy by up to 19.4% over REDF and decreasing detection latency by up to 17.5%.

Qualitative Description of Metal Ore Deposits Parameters Based on Selected Fuzzy Logic Operators on the Example of a KGHM Polish Copper S.A. Copper-Silver Mine

Qualitative Description of Metal Ore Deposits Parameters Based on Selected Fuzzy Logic Operators on the Example of a KGHM Polish Copper S.A. Copper-Silver Mine

Hesitant fuzzy β-covering ( T , I ) rough set models: An application to multi-attribute decision-making

Covering rough sets have been successfully applied to decision analysis because of the strong representing capability for uncertain information. As a research hotspot in decision analysis, hesitant fuzzy multi-attribute decision-making (HFMADM) has received increasing attention. However, the existing covering rough sets cannot handle hesitant fuzzy information, which limits its application. To tackle this problem, we set forth hesitant fuzzy β-covering rough set models and discuss their application to HFMADM. Specifically, we first construct four types of hesitant fuzzy β-covering ( T , I ) rough set models via hesitant fuzzy logic operators and hesitant fuzzy β-neighborhoods, which can handle hesitant fuzzy information without requiring any prior knowledge other than the data sets. Then, some intriguing properties of these models and their relationships are also discussed. In addition, we design a new method to deal with HFMADM problems by combining the merits of the proposed models and the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. In this method, we not only consider the risk preferences of decision-makers, but also present a new hesitant fuzzy similarity measure expressed by hesitant fuzzy elements to measure the degree of closeness between two alternatives. Finally, an enterprise project investment problem is applied to illustrate the feasibility of our proposed method. Meanwhile, the stability and effectiveness of our proposed method are also verified by sensitivity and comparative analyses.

Why are Discrete Implications Necessary? An Analysis Through the Discretization Process

Discrete implications have been studied for almost two decades as those operators needed to perform inference processes when dealing with qualitative information from a finite chain. However, it is known that by means of some adequate transformations, fuzzy logic operators defined in [0,1] can generate the corresponding discrete operators. Thus, an immediate question arises: do we need to study discrete implications or is it enough to study implications defined in [0,1] and then to discretize them? The answer must rely on the preservation of the additional properties of fuzzy implication functions through these discretization methods. In this paper, for two specific methods based on the ceiling and floor functions it is proved that most of the additional properties are not preserved in general, showing that the preservation of the additional properties depends directly on the properties of the underlying operators considered in the discretization. Thus, sufficient, and for some properties necessary, conditions to guarantee the preservation are presented.

On residual implications derived from 2-uninorms

Residual implications are one of the most important families of fuzzy implications, which are related to the concept of residuation in intuitionistic logic. In this paper, we focus on residual operators derived from 2-uninorms and study necessary and sufficient conditions for such operators being fuzzy implications, called RU2-implications. What is remarkable is that the RU2-implication is a generalization of residual implications generated by some popular fuzzy logic operations such as t-norms, uninorms and uni-nullnorms. Then, we obtain the block structures for RU2-implications, especially for ones generated by two special classes of conjunctive 2-uninorms. Moreover, the main properties of RU2-implications and their natural negations are discussed. Finally, we give a characterization of residual implications derived from left-continuous 2-uninorms, which preserve the residuation property.

Analysis and Prioritization of the Factors of the Robotic Industry With the Assistance of EDAS Technique Based on Intuitionistic Fuzzy Rough Yager Aggregation Operators

The focus of the interdisciplinary and scientific discipline of robotics is to design, maintain and use mechanical robotics. There exist many issues faced by the robotic industry but there are some factors that can cover these complexities effectively. Handling vague and imprecise data is a difficult task nowadays. So there is a need to define such kind of effective and valuable tool that can handle complex and vague data more dominantly. The evaluation based on distance from average solution (EDAS) method is a very useful tool that can handle complex data more effectively. The best alternative can be chosen based on distance from the average solution. The EDAS method is relatively simple to use and provide a quick evaluation of alternative based on multiple criteria. Yager t-norm and t-conorm are two fuzzy logic operators proposed by Yager. So based on the importance of Yager t-norm and t-conorm, initially, in this article, we have proposed the basic operative laws for intuitionistic fuzzy rough numbers. Based on these developed operational laws, we have developed some new intuitionistic fuzzy rough aggregation operators called intuitionistic fuzzy rough Yager average (weighted, ordered weighted, hybrid) aggregation operators and intuitionistic fuzzy rough Yager geometric (weighted, ordered weighted, hybrid) aggregation operators. Moreover, we have proposed the EDAS technique based on intuitionistic fuzzy rough Yager aggregation operators and used these notions for the selection of suitable factors that play a vital role in the robotic industry. Also, to show the effective use of these introduced notions, we have proposed an algorithm for the EDAS method based on intuitionistic fuzzy rough Yager aggregation operators along with a descriptive example. To show the superiority of the introduced work we have developed a comparative analysis.

Аппаратная реализация встраиваемых нечетких систем

Principles of using analog circuitry for implementing basic fuzzy logic operations are considered. A set of developed hardware modules, where operations are executed on an analog representation of fuzzy variables is presented. Modules for converting information from digital to continuous form and vice versa are presented in order to link fuzzy system with sensors and executing circuitry, for example, in the control system of a process or an object. The components of such modules are described, along with their pros and cons. The use of circuits based on these principles seems essential for the development of fuzzy electronics.