Fuzzy Integral Method Research Articles

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.

Fuzzy PIPRECIA and CRADIS integrated method in electric vehicle selection

Fuzzy PIPRECIA and CRADIS integrated method in electric vehicle selection

Research on Case Reasoning Multi-attribute Group Decision-making Method Based on Hesitant Fuzzy Set

In this study, a hesitant fuzzy set-based case-based reasoning integration method is proposed for the multi-attribute group decision-making problem with unknown attribute weights and mixed forms of attribute values. First, from two perspectives, traditional distance measure and information theory, a multi-objective optimization model is constructed using the distance similarity measure and information entropy of each type of attributes to determine the attribute weights. Secondly, considering the hybrid and nonlinear characteristics of case data, based on the principle of symmetric interaction entropy and TOPSIS method, a global similarity measure based on symmetric interaction entropy is proposed and a case inference algorithm suitable for hesitant fuzzy environment is designed. Finally, by analyzing the arithmetic cases of the target case in the case base, the most similar historical cases to the target case are retrieved to determine the decision-making scheme, and the practicality and feasibility of the decision-making method are verified. The results show that considering hesitant fuzzy theory for case-based reasoning research will help improve the accuracy and reliability of decision-making and provide more effective support for multi-attribute group decision management.

Barrier identification, analysis and solutions of blockchain adoption in construction: a fuzzy DEMATEL and TOE integrated method

PurposeBlockchain has the potential to facilitate a paradigm shift in the construction industry toward effectiveness, transparency and collaboration. However, there is currently a paucity of empirical evidence from real-world construction projects. This study aims to systematically review blockchain adoption barriers, investigate critical ones and propose corresponding solutions.Design/methodology/approachAn integrated method was adopted in this research based on the technology–organization–environment (TOE) theory and fuzzy decision-making trial and evaluation laboratory (DEMATEL) approach. Blockchain adoption barriers were first presented using the TOE framework. Then, key barriers were identified based on the importance and causality analysis in the fuzzy DEMATEL. Several suggestions were proposed to facilitate blockchain diffusion from the standpoints of the government, the industry and construction organizations.FindingsThe results highlighted seven key barriers. Specifically, the construction industry is more concerned with environmental barriers, such as policy uncertainties (E2) and technology maturity (E3), while most technical barriers are causal factors, such as “interoperability (T4)” and “smart contracts' security (T2)”.Practical implicationsThis study contributes to a better understanding of the problem associated with blockchain implementation and provides policymakers with recommendations.Originality/valueIdentified TOE barriers lay the groundwork for theoretical observations to comprehend the blockchain adoption problem. This research also applied the fuzzy method to blockchain adoption barrier analysis, which can reduce the uncertainty and subjectivity in expert evaluations with a small sample.

An Integrated Fuzzy MCDM Method for the Evaluation of R&D Projects

Research and development (R&D) activities are essential to guarantee continuity of firms, meet customer requirements and keep ahead in competition. R&D project selection constitutes a significant part of project management in order to achieve the desired results and outputs. In this study, an integrated fuzzy multi-criteria group decision making approach is developed for R&D project selection. The problem includes a hierarchical structure of the criteria, uncertainty in evaluating the relative importance of criteria/sub-criteria and rating of candidate projects. The method employs the ordered weighted average (OWA) operator as the aggregation operator, which helps to fully reflect the real behavior of the decision makers in group decision making problems. Fuzzy integral method, which does not require the assumption of the mutual independence of criteria, is used to rank the alternatives. The case study is conducted in a small-sized company in Turkey, which designs and produces special purpose machines. A R&D project selection model is developed to maximize the desired outputs. The results of the analysis show that technological, environmental, marketing, organizational, national and financial issues should be considered simultaneously in the evaluation process. The proposed method is shown to be efficient, generalizable and practical and it has several significant merits compared to the other methods.

Spectrum Sensing Algorithm by Multi-Objective Optimization Theory and Fuzzy Integral Method

Spectrum Sensing Algorithm by Multi-Objective Optimization Theory and Fuzzy Integral Method

Impact angle constrained fuzzy adaptive fault tolerant IGC method for Ski-to-Turn missiles with unsteady aerodynamics and multiple disturbances

An impact angle constrained fuzzy adaptive fault tolerant integrated guidance and control method for Ski-to-Turn (STT) missiles subject to unsteady aerodynamics and multiple disturbances is proposed. Unsteady aerodynamics appears when flight vehicles are in a transonic state or confronted with unstable airflow. Meanwhile, actuator failures and multisource model uncertainties are introduced. However, the boundaries of these multisource uncertainties are assumed unknown. The target is assumed to execute high maneuver movement which is unknown to the missile. Furthermore, impact angle constraint puts forward higher requirements for the interception accuracy of the integrated guidance and control (IGC) method. The impact angle constraint and the precise interception are established as the object of the IGC method. Then, the boundaries of the lumped disturbances are estimated, and several fuzzy logic systems are introduced to compensate the unknown nonlinearities and uncertainties. Next, a series of adaptive laws are developed so that the undesirable effects arising from unsteady aerodynamics, actuator failures and unknown uncertainties could be suppressed. Consequently, an impact angle constrained fuzzy adaptive fault tolerant IGC method with three loops is constructed and a perfect hit-to-kill interception with specified impact angle can be implemented. Eventually, the numerical simulations are conducted to verify the effectiveness and superiority of the proposed method.

A Fuzzy Imperfect Production Inventory Model Based on Fuzzy Differential and Fuzzy Integral Method

In the inventory theory, to treat the uncertainty, the fuzzy set concept is used in order to provide a feasible approach to deal with the uncertainty problem. In this research work, a fuzzy economic production quantity model with interactive fuzzy demands is proposed. In a production process, in the beginning, the system is assumed to be in a controlled state in which only perfect items are manufactured. Later, the manufacturing production process shifts to be an out-of-control-state system; producing both perfect and imperfect items simultaneously, this is considered as a fuzzy state. The defective production rate is also taken into account as a fuzzy state. Here, the selection process of produced items is realized during the production period. With the aim of studying the practical feasibility of the fuzzy economic production inventory model along with a sensitivity analysis of some parameters, different numerical examples are illustrated.

Power Intelligent Terminal Intrusion Detection Based on Deep Learning and Cloud Computing.

Numerous internal and external intrusion attacks have appeared one after another, which has become a major problem affecting the normal operation of the power system. The power system is the infrastructure of the national economy, ensuring that the information security of its network not only is an aspect of computer information security but also must consider high-standard security requirements. This paper analyzes the intrusion threat brought by the power information network and conducts in-depth research and investigation combined with the intrusion detection technology of the power information network. It analyzes the structure of the power knowledge network and cloud computing through deep learning-based methods and provides a network interference detection model. The model combines the methods of abuse detection and anomaly detection, which solves the problem that the abuse analysis model does not detect new attack variants. At the same time, for big data network data retrieval, it retrieves and analyzes data flow quickly and accurately with the help of deep learning of data components. It uses a fuzzy integral method to optimize the accuracy of power information network intrusion prediction, and the accuracy reaches 98.11%, with an increase of 0.6%.

Anti-Poverty Performance Assessment and Forecasting

In order to further help each unit that mitigates the poverty of impoverished regions to improve the efficiency of poverty alleviation, this paper establishes a poverty alleviation performance evaluation index system. Firstly, we analyze the correlation among poverty alleviation indicators through a matrix density diagram. Second, the questions are divided into target layer (poverty alleviation performance), criterion layer (poverty alleviation efficiency, poverty alleviation effect, and poverty alleviation rate), and indicator layer, and the fuzzy hierarchical integrated evaluation method is used to calculate the performance score of each poverty alleviation unit. In addition, based on the historical data, support vector machines were used to calculate the grading rules and predict whether the village could be awarded the title of "advanced village in poverty alleviation". Finally, based on the study, recommendations were made to the National Poverty Alleviation Office for the differences in the individuality of the villages and the distribution of the work content of the poverty alleviation units.

Spatial Identification and Redevelopment Evaluation of Brownfields in the Perspective of Urban Complex Ecosystems: A Case of Wuhu City, China.

Rapid industrialization and urbanization in China have led to a rapid increase in the number of brownfields, however there is a lack of identification of the spatial extent of brownfields in cities and accurate assessment of brownfield redevelopment. Based on the relationship between brownfields and urban complex ecosystems, this paper defines brownfields in China and constructs a comprehensive evaluation index system including socio-economic and ecological subsystems. Using Wuhu City as empirical evidence, 19 brownfields were identified using remote sensing data and field surveys. Based on the detection of soil contaminants in brownfields, a fuzzy integrated evaluation method was used to suggest their redevelopment direction. It is found that the government’s planned land use types and the brownfield redevelopment evaluation results match to a large extent, but social, economic and ecological environmental factors should be more fully considered. At the same time, the identification and redevelopment of brownfield sites in the city as a whole need to be carried out by the government’s professional forces in order to obtain more effective and scientific conclusions.

A Study on Single Pilot Resource Management Using Integral Fuzzy Analytical Hierarchy Process

This research aims to help develop aviation safety policies for the general aviation industry, especially for flight training schools. The analytical hierarchy process (AHP), fuzzy AHP, and fuzzy integral methods were used to find variables that impact aviation safety for training pilots in Korea and the United States using survey participants’ experience and perceptions. The results represent the circumstances of aviation safety in the real world where single pilot resource management, especially situational awareness, is crucial. The authors find that integral fuzzy AHP provides more explicit considerations, making up for the ambiguity of the linguistic responses caused by the AHP and fuzzy AHP.

Fuzzy FMEA for Biology-Guided Radiotherapy (BgRT)

<h3>Purpose/Objective(s)</h3> Biology-guided Radiotherapy (BgRT) is a new external beam radiotherapy delivery modality combining positron emission tomography-computed tomography (PET-CT) with a linear accelerator. Our research group has previously introduced the BgRT specific prospective process map for developing an initial clinical workflow and has performed the risk analysis for the BgRT process that will bring in a new treatment paradigm. However, due to a lack of experience and historical data, assigning an accurate value of each risk factor in traditional FMEA leads to difficulties for evaluators. Also, there are certain sub-processes where the differences in weights of risk factors needs to be considered. Therefore, in this study, we introduced the fuzzy FMEA to perform a comprehensive analysis of failure modes (FMs) of BgRT and overcome weaknesses of conventional FMEA. <h3>Materials/Methods</h3> Five sub-processes unique to BgRT out of a total of 15 sub-processes were selected and relevant 84 FMs were identified. The evaluators used the fuzzy linguistic variables (very low, low, medium, high, and very high) to evaluate the importance of risk factors for assigning weights as well as the evaluation of each risk factor (occurrence, severity, and detectability) of each failure mode. Then, the fuzzy measure was used to calculate the weights of risk factors and the fuzzy integral method was used to fuse every value of risk factor of FMs to obtain the final fuzzy risk priority number value. <h3>Results</h3> Top 3 FMs are the machine down/emergency during beam delivery, failure to meet activity before PET pre-scan, and failure/delay of tracer delivery from a local vendor. The mitigation plans are discussed by team members including complete guidelines for the emergencies on the treatment machine, maintenance program of PET tracers by dedicated staffs, and staff training program for the BgRT procedure. Strong cooperation with a local PET tracer vendor and timely patient scheduling to allow multiple PET images at the treatment machine were also included in mitigation plans. <h3>Conclusion</h3> The proposed fuzzy FMEA presents a more reasonable and effective method for assessing potential failures allowing fuzzy linguistic variables terms that are easier to use than numerical assignments to evaluate the FMs, especially with the new prototype machine for BgRT. The results have been shared with team members, and mitigation plans will be implemented.

Performance Analysis of Data Fusion Methods Applied to Epileptic Seizure Recognition

Abstract Epilepsy is a chronic neurological disorder that is caused by unprovoked recurrent seizures. The most commonly used tool for the diagnosis of epilepsy is the electroencephalogram (EEG) whereby the electrical activity of the brain is measured. In order to prevent potential risks, the patients have to be monitored as to detect an epileptic episode early on and to provide prevention measures. Many different research studies have used a combination of time and frequency features for the automatic recognition of epileptic seizures. In this paper, two fusion methods are compared. The first is based on an ensemble method and the second uses the Choquet fuzzy integral method. In particular, three different machine learning approaches namely RNN, ML and DNN are used as inputs for the ensemble method and the Choquet fuzzy integral fusion method. Evaluation measures such as confusion matrix, AUC and accuracy are compared as well as MSE and RMSE are provided. The results show that the Choquet fuzzy integral fusion method outperforms the ensemble method as well as other state-of-the-art classification methods.

Hybrid force/position control in workspace of robotic manipulator in uncertain environments based on adaptive fuzzy control

In this study, a novel hybrid force/position controller in workspace of robotic manipulator based on adaptive fuzzy control is developed to improve the controlling performance of force and position in contact with uncertain environments. The dynamic model of the robotic manipulator in joint space is converted to a dynamic model in workspace, which is consisted of the model of position-controlled subsystem and model of force-controlled subsystem. Furthermore, in the position-controlled subsystem, an adaptive fuzzy computed torque control is proposed by considering adaptive fuzzy control and conventional computed torque control (AFCTC), for compensating deviations caused by the presence of structured uncertainty and unstructured uncertainty. In the force-controlled subsystem, a fuzzy proportional integral control method (FPI) is proposed by fuzzy logic and conventional proportional integral method to improve the control performance. The asymptotic stability of the developed controller is proved by Lyapunov theorem. The simulation results show the preferable performance of the proposed controller (AFCTC-FPI) by comparison with adaptive fuzzy sliding mode control (AFSMC), adaptive network-based fuzzy inference system with proportion differential and integral (ANFIS-PD+I), computed torque control-proportion integral (CTC-PI), fuzzy proportional integral derivative (FPID), and proportional integral derivative (PID) in uncertain environments. Furthermore, in the experimental study, average position error with AFCTC-FPI is decreased by 87.14 % than that with PID, meanwhile, range of interaction force with AFCTC-FPI is reduced by 70.31% than that with PID. In summary, the experimental results also show the superior control accuracy of the proposed controller in real environment.

Study on Promoting Intelligent Manufacturing Path Choice of Manufacturing Enterprises Based on Coevolution Strategy

The basic guarantee of promoting intelligent manufacturing ability of manufacturing enterprises is to enhance the comprehensive strength of the country and realize industrialization. Based on the coevolution strategy, considering the time and space structure, this paper determines that the path of manufacturing enterprises to promote intelligent manufacturing is adaptive, visionary, planned, and emerging. According to the factors influencing the promotion of intelligent manufacturing by manufacturing enterprises, the paper proposes the following steps: adaptive strategy path, vision strategic path, planned strategic path, and emerging strategy path. The paper constructs an evaluation system for manufacturing enterprises to promote the selection of intelligent manufacturing path with 25 evaluation indexes and establishes the three-dimensional model of promoting intelligent manufacturing path selection by using the combination of Choquet fuzzy integral method and SPACE method. Through the empirical analysis of ARR, it is found that the planned strategic path is the most suitable path to promote intelligent manufacturing, and the scientificity and objectivity of the three-dimensional path selection model are shown. It is of great theoretical innovation and practical significance to develop a new perspective for manufacturing enterprises to promote the research and selection of intelligent manufacturing path.

Intelligent Computer-Aided Prostate Cancer Diagnosis Systems: State-of-the-Art and Future Directions

Prostate Cancer (PCa) is one of the common cancers among men in the world. About 16.67% of men will be affected by PCa in their life. Due to the integration of magnetic resonance imaging in the current clinical procedure for detecting prostate cancer and the apparent success of imaging techniques in the estimation of PCa volume in the gland, we provide a more detailed review of methodologies that use specific parameters for prostate tissue representation. After collecting over 200 researches on image-based systems for diagnosing prostate cancer, in this paper, we provide a detailed review of existing computer-aided diagnosis (CAD) methods and approaches to identify prostate cancer from images generated using Near-Infrared (NIR), Mid-Infrared (MIR), and Magnetic Resonance Imaging (MRI) techniques. Furthermore, we introduce two research methodologies to build intelligent CAD systems. The first methodology applies a fuzzy integral method to maintain the diversity and capacity of different classifiers aggregation to detect PCa tumor from NIR and MIR images. The second methodology investigates a typical workflow for developing an automated prostate cancer diagnosis using MRI images. Essentially, CAD development remains a helpful tool of radiology for diagnosing prostate cancer disease. Nonetheless, a complete implementation of effective and intelligent methods is still required for the PCa-diagnostic system. While some CAD applications work well, some limitations need to be solved for automated clinical PCa diagnostic. It is anticipated that more advances should be made in computational image analysis and computer-assisted approaches to satisfy clinical needs shortly in the coming years.

Binary Ghost Imaging Based on the Fuzzy Integral Method

The reconstruction quality of binary ghost imaging depends on the speckle binarization process. In order to obtain better binarization speckle and improve the reconstruction quality of binary ghost imaging, a local adaptive binarization method based on the fuzzy integral is proposed in this study. There are three steps in the proposed binarization process. The first step is to calculate the integral image of the speckle with the summed-area table algorithm. Secondly, the fuzzy integral image is calculated through the discrete Choquet integral. Finally, the binarization threshold of each pixel of the speckle is selected based on the calculated fuzzy integral result. The experiment results verify the feasibility of the proposed method. Compared with other methods qualitatively and quantitatively, the proposed method has high performance in reconstructing image of target.

Wind Farm Location Special Optimization Based on Grid GIS and Choquet Fuzzy Integral Method in Dalian City, China

Selecting an appropriate wind farm location must be specific to a particular administrative region, which involves restrictions balance and trade-offs. Multi-criteria decision making (MCDM) and GIS are widely used in wind energy planning, but have failed to achieve the selection of an optimal location and make it difficult to establish a set of independent factors. Fuzzy measurement is an effective method to evaluate intermediate synthesis and calculates the factor weight through fuzzy integrals. In this paper, optimal wind farm location is analyzed through coupling Grid GIS technique with λ fuzzy measure. Dalian City is selected as the study area for proving the feasibility of the proposed method. Typography, meteorological, transmission facilities, biological passage, and infrastructure are taken into the index system. All the indexes are specialized into victor grid cells which are taken as the base wind farm location alternative unit. The results indicate that the Grid GIS based λ fuzzy measure and Choquet fuzzy integral method could effectively deal with the special optimization problem and reflect optimal wind farm locations.

Combination of Pattern Classifiers Based on Naive Bayes and Fuzzy Integral Method for Biological Signal Applications

Background: Achieving the best possible classification accuracy is the main purpose of each pattern recognition scheme. An interesting area of classifier design is to design for biomedical signal and image processing. Materials and Methods: In the current work, in order to increase recognition accuracy, a theoretical frame for combination of classifiers is developed. This method uses different pattern representations to show that a wide range of existing algorithms could be incorporated as the particular cases of compound classification where all the pattern representations are used jointly to make an accurate decision. Results: The results show that the combination rules developed under the Naive Bayes and Fuzzy integral method outperforms other classifier combination schemes. Conclusion: The performance of different combination schemes has been studied through an experimental comparison of different classifier combination plans. The dataset used in the article has been obtained from biological signals.