Fuzzy Opinion Research Articles

Risk assessment of disconnect operations of MNPP using similarity aggregation method based fuzzy dynamic Bayesian networks model

Marine Nuclear Power Platforms (MNPP) have the potential to provide a safe and efficient energy supply for offshore oil extraction and remote islands. During the service, MNPP is subject to disconnect operations and return to the dock for repair and maintenance. However, the failure of disconnect operations of MNPP can have catastrophic consequences. It is essential to carry out an accurate risk assessment of the disconnect operation process. In this study, a comprehensive dynamic quantitative dynamic risk assessment method is proposed to study the whole process of MNPP disconnect operations from preparation to towing. Firstly, a similarity aggregation method (SAM) is used to solve the expert fuzzy opinion aggregation problem, and then the probability of failure of the event consistent with the group consensus opinion is obtained. Secondly, a dynamic Bayesian networks (DBNs) model is developed for three stages, including YOKE offloading operation, YOKE dropping operation, and equipment recovery operation, and then predicts the dynamic probability of failure during the disconnect operations. Finally, the prediction, sensitivity, and diagnostic analysis techniques in DBNs are used to simulate the evolution of the whole risk system. Meanwhile, the basic events probabilities were updated based on new evidence, and the most likely causes of accidents were found. Safety control measures are developed to effectively reduce the risks faced by the MNPP in the disconnect operations.

Fuzzy dynamic Bayesian network based on a discrete aggregation method for risk assessment of marine nuclear power platform hinge joints accidents

Hinge joints play a critical role in the positioning system of Marine Nuclear Power Platforms (MNPP) by facilitating the transmission of mooring forces and accommodating rotational motions of the hull. Failure of hinge joints during operation can result in severe consequences for both the environment and human safety. Therefore, this study aims to address the limitations associated with the dynamic characteristics and uncertain information pertaining to the risk systems of hinge joint structures in MNPP. To tackle the challenge of conflicting expert opinions in group decision-making, a novel method called the discrete aggregation method (DAM) is proposed. DAM utilizes fuzzy opinion aggregation, taking into account the degree of consensus among experts. This approach effectively overcomes the problem of conflicting opinions. Furthermore, building a hinge joint accidents model using Bow-tie (BT) for hazard identification, and then mapping the BT model to a dynamic Bayesian networks (DBN) model. Fuzzy set theory (FST) and DBN are employed to handle cognitive uncertainty caused by data limitations and incomplete knowledge. These techniques enable causal inference for time-varying dynamic risk systems. To validate the proposed method, a case study of the positioning system of the first MNPP in China is conducted. The dynamic fatigue failure probability of hinge joints is calculated using predictive technology based on Bayesian theory. Additionally, diagnostic analysis techniques are employed to accurately identify key events and their underlying causes. The analysis results provide valuable data support for on-site operations and management personnel, aiding in the development of effective risk mitigation measures.

Consensus in fuzzy opinion networks

This paper developed the dynamics of opinion network where a node interacts with only one node in each step and these nodes will not exchange their opinions until the difference of their opinions is below a tolerance threshold. Every node is a Gaussian fuzzy set with the center representing an opinion itself and a standard deviation characterizing an uncertainty about the opinion. The fuzzy opinion network with different uncertainties’ levels of nodes was investigated to show how opinions and their uncertainties propagate and evolve for reaching a consensus in the network. The theoretical and numerical analyses were used to assess the conditions where a consensus can be reached in the fuzzy opinion network.

Study on the Regularity of Surface Collapse in Goaf using UAV Remote Sensing Technology

UAV remote sensing technology is used to analyze the characteristics of surface collapse and evaluate the degree of damage in the mining area, and the regularity of surface collapse in the mining area is studied. The distribution and variation characteristics of surface collapse pits and surface cracks and surface deformation characteristics are obtained from UAV images. Taking the area of collapse pits, the depth of collapse pits, the width of cracks, the drop of cracks, the density of cracks, surface subsidence and surface inclination as the evaluation factors, the fuzzy opinion centralized decision-making of the classical fuzzy comprehensive evaluation method is introduced into the combined weighting method, and the fuzzy comprehensive evaluation model based on AHP-CRITIC method is established. The results show that: (1) From August 2020 to June 2021, 125 surface cracks have increased; Three new collapse pits are generated, with a maximum area of about 323m2 and a maximum depth of about 21m; The surface deformation in the south of the mining area is obvious, forming a settlement funnel. (2) The severe damage degree of surface collapse is mainly distributed near the surface collapse pits, the moderate damage degree is mainly distributed in the central and southern part of the study area, and the mild and slight damage degree is mainly distributed in the periphery of the ore body.

Theoretical development of a probabilistic fuzzy model for opinion formation in social networks

The vagueness in human opinions, the randomness in the interactions among individuals, and the nonlinearities in opinion updates are challenging issues in modeling opinion formation and consensus. In this paper, we address the theoretical foundation and the necessary conditions for the convergence of opinion formation's nonlinear dynamics to the true states. For this purpose, we extend a probabilistic fuzzy model of opinion formation to include the effect of perceiving random and vague media content on the uncertainty of individuals' opinions. In this model, each individual faces an uncertain media that is statistically modeled. Hence the individual perceives a probabilistic fuzzy set of possible environmental states and shapes its probabilistic fuzzy opinion (belief) based on its perceptions and negotiations. Theoretical results reveal that the media contents should be statistically supportive of the true state and that the coefficients in the belief update algorithms should be selected within the extracted bounds for the perception aggregation process and the proposed optimistic and pessimistic assuring operators. Application to two networks with different sizes of 94 and 918 nodes with 1134 and 206869 edges, respectively, confirms these theoretical conclusions. These simulation results show that higher diversity is observed in the opinions of the highly connected individuals. Furthermore, convergence is delayed for the larger network, i.e. there remain few individuals with differing opinions until the late stages of convergence. It is also shown that model convergence to the true state may deteriorate if parameter settings do not adhere to the theoretically obtained bounds.

Extension of interval-valued Pythagorean FDOSM for evaluating and benchmarking real-time SLRSs based on multidimensional criteria of hand gesture recognition and sensor glove perspectives

Several DataGlove wearable electronic devices based on real-time Sign Language Recognition Systems (SLRSs) have been developed recently to assist the deaf and dumb community in translating hand gestures to their spoken language equivalents. Multidimensional evaluation and benchmarking of these systems are critical for determining the most desirable approach for meeting all essential requirements. However, this process is considered a Multicriteria Decision-Making (MCDM) problem due to the presence of several issues, including multiple evaluation criteria, criteria importance and criteria confliction. Hence, the MCDM approach is required to solve these issues. In this study, a new extension of the Fuzzy Decision by Opinion Score Method (FDOSM) for evaluating and benchmarking SLRSs is developed under an Interval-Valued Pythagorean Fuzzy Set (IVPFS) named IVP-FDOSM. Fundamentally, the methodology includes two phases. In the first phase, a decision matrix is formulated on the basis of identified ‘multidimensional criteria of hand gesture recognition and sensor glove perspectives’ and ‘real-time SLRSs’. The second phase introduces the development of IVP-FDOSM in two stages. The decision matrix is transformed into an opinion matrix in the first stage (data transformation unit). Meanwhile, in the second stage (data-processing unit), the opinion matrix is converted into fuzzy opinion decision matrices with the assistance of three experts by transforming the opinion matrix’s linguistic terms to Interval-Value Pythagorean Fuzzy Numbers (IVPFNs). Results indicate the following: (1) individual benchmarking results of real-time SLRS showed high variation (90%) based on the preference of each Decision Maker (DM), with only 10% of preferences being identical. (2) The results of group benchmarking reveal that the 10th real-time SLRS was the optimal one and the 6th was the worst. In addition, the rates of ranking match between the group benchmarking and each DM (first DM, second DM and third DM) were 23%, 37% and 43%, respectively. (3) For the results evaluation, the statistical test-based objective assessment shows that the first group received the lowest mean value (2.80333), while the third group received the highest mean (3.8). These values indicate that the group benchmarked systems resulting from IVP-FDOSM are undergoing a systematic ranking. Furthermore, the comparative analysis reveals that IVP-FDOSM is superior to IVP-TOPSIS and IVP-AHP in terms of ranking and weighting.

A Fuzzy Delphi Consensus Methodology Based on a Fuzzy Ranking

Delphi multi-round survey is a procedure that has been widely and successfully used to aggregate experts’ opinions about some previously established statements or questions. Such opinions are usually expressed as real numbers and some commentaries. The evolution of the consensus can be shown by an increase in the agreement percentages, and a decrease in the number of comments made. A consensus is reached when this percentage exceeds a certain previously set threshold. If this threshold has not been reached, the moderator modifies the questionnaire according to the comments he/she has collected, and the following round begins. In this paper, a new fuzzy Delphi method is introduced. On the one hand, the experts’ subjective judgments are collected as fuzzy numbers, enriching the approach. On the other hand, such opinions are collected through a computerized application that is able to interpret the experts’ opinions as fuzzy numbers. Finally, we employ a recently introduced fuzzy ranking methodology, satisfying many properties according to human intuition, in order to determine whether the expert’s fuzzy opinion is favorable enough (comparing with a fixed fuzzy number that indicates Agree or Strongly Agree). A cross-cultural validation was performed to illustrate the applicability of the proposed method. The proposed approach is simple for two reasons: it does not need a defuzzification step of the experts’ answers, and it can consider a wide range of fuzzy numbers not only triangular or trapezoidal fuzzy numbers.

Fuzzy inference based Hegselmann–Krause opinion dynamics for group decision-making under ambiguity

This paper considers a group decision-making mechanism for a group of social agents with ambiguous interactions. First, a fuzzy inference approach is introduced to describe bounded confidence based interaction rules among social agents for a certain object in a social network platform. Second, an influence graph is introduced to model the communication network topology associated with the group of agents. A fuzzy inference based opinion dynamics model is built when the defuzzified interaction weights among agents are used in the opinion update scheme for each agent. Third, the patterns of the collective final opinions are analyzed under the proposed fuzzy opinion dynamics model. The fuzziness of opinion gaps and interaction weights among agents are respectively investigated in the collective opinion evolution. Simulation results show the influence of fuzzy values of opinion gaps and interaction weights on the pattern of the collective final opinions and reveal the quantitative relationships of opinion gap and interaction weights between the original HK opinion dynamics model and the fuzzy HK opinion dynamics model.

Modeling Opinion Formation in Social Networks: A Probabilistic Fuzzy Approach

Opinion formation in social networks is an interesting dynamical process from the perspective of system modeling due to its large scale as well as the variety of structural and parametric uncertainties that it entails. This paper proposes a probabilistic fuzzy opinion formation model for predicting the opinions of communities in the social networks. In this regard, the opinions of a group of individuals about a given topic in a Telegram pilot group, as a popular social network, are collected and presented in the framework of the probabilistic fuzzy model. Based on the obtained data, the parameters of the model are extracted, and the model is tuned. Finally, the variations of the actual opinions throughout time are compared with the model predictions. The numerical results in this study show that, with appropriately tuned parameters, the model successfully represents the opinion formation process, with an average error that approaches zero.

Hierarchical Fuzzy Opinion Networks: Top-Down for Social Organizations and Bottom-Up for Election

A fuzzy opinion is a Gaussian fuzzy set with the center representing the opinion and the standard deviation representing the uncertainty about the opinion, and a fuzzy opinion network is a connection of a number of fuzzy opinions in a structured way. In this paper, we propose: (a) a top-down hierarchical fuzzy opinion network to model how the opinion of a top leader is penetrated into the members in social organizations, and (b) a bottom-up fuzzy opinion network to model how the opinions of a large number of agents are agglomerated layer-by-layer into a consensus or a few opinions in the social processes such as an election. For the top-down hierarchical fuzzy opinion network, we prove that the opinions of all the agents converge to the leaders opinion, but the uncertainties of the agents in different groups are generally converging to different values. We demonstrate that the speed of convergence is greatly improved by organizing the agents in a hierarchical structure of small groups. For the bottom-up hierarchical fuzzy opinion network, we simulate how a wide spectrum of opinions are negotiating and summarizing with each other in a layer-by-layer fashion in some typical situations.

Risk Identification and Assessment of the Accidents for Distance Water Transfer Project

For South to North Water Transfer Project (SNWTP) in China with a long open canal, the distance water transfer system is a complex river system communicating with rivers and lakes along the way. Water delivery safety is threatened by various potential accidents. In this paper, using check list method, the accident risk was identified and the accident types were distinguished. Then, using fuzzy opinion centralized decision method (FOCD) and analytic hierarchy process (AHP), the risk assessment system was established and the assessment model was developed. Finally, the accident risk and the influence degree of various types of accidents for SNWTP in Shandong Province were evaluated.

Multi-Attribute Group-Decision on a Construction Diversion Scheme for Hydropower Projects Based on Perception Utility

The multi-attribute and group-decision problem in the selection of a construction diversion scheme for large hydropower projects often involves multi-decision subjects and schemes. Each scheme includes multiple attributes, and the attribute values and weights are multi-attribute group decision-making problems with interval numbers. In this study, a new method for solving the multi-attribute group-decision problem is proposed by integrating regret theory, negotiation gathering theory, and the Monte Carlo simulation technique. Firstly, decision-makers’ comprehensive perception utility for each scheme is calculated based on the regret theory. Then, non-uniform and fuzzy opinion of different decision subjects are negotiated and gathered, and negotiation intervals of the attribute weights are calculated through group negotiation gathering theory. Moreover, fuzzy complementary judgment matrixes describing an excellent degree of the diversion schemes are obtained by conducting Monte Carlo simulations. Finally, the alternative diversion schemes are sorted in terms of their priorities, and the reliability of the sorting procedure is confirmed. The multi-attribute group-decision problem in the selection of a construction diversion scheme for Jinping I Hydropower Station is effectively solved by the proposed method. The proposed method is reliable and may significantly contribute to engineering decision-making.

Distributed prediction of solar farm status by probabilistic fuzzy opinion formation model

Although photovoltaic arrays are a highly reliable solution to generating energy, the full scale use of these integrated arrays presents formidable challenges since their future generation carries high uncertainty. In this paper, the success of a large-scale solar farm in generating different percentages of its committed value is forecasted by a new opinion formation model. In the proposed model, each node uses local generation data, transforms them to a fuzzy set called perception, and evolves its fuzzy opinion about the entire farm's future generation by using this perception and negotiating with other nodes. As a result, the fuzzy predictions, which can be used in fuzzy energy management systems, are achieved with simple, distributed, and scalable computations. As a case study, Belgium is considered here as the entire solar farm with its provinces as the local generation units. To evaluate the predicted fuzzy opinions, we extract the lower/upper and point-wise predictions from them and compare these extracted values with the outputs of central data-driven forecasting algorithms. Furthermore, to evaluate the proposed aggregation mechanism, the obtained opinions are compared with the fusion of perceptions by conventional fusion algorithms. The results show that in both experiments, the proposed strategy performs better than its competitors in the sense of maximum absolute percentage error and mean-squared error.

Fuzzy Opinion Networks: A Mathematical Framework for the Evolution of Opinions and Their Uncertainties Across Social Networks

We propose a new mathematical framework for the evolution and propagation of opinions, called fuzzy opinion network, which is the connection of a number of Gaussian nodes, possibly through some weighted average, time delay, or logic operators, where a Gaussian node is a Gaussian fuzzy set with the center and the standard deviation being the node inputs and the fuzzy set itself being the node output. In this framework, an opinion is modeled as a Gaussian fuzzy set with the center representing the opinion itself and the standard deviation characterizing the uncertainty about the opinion. We study the basic connections of fuzzy opinion networks, including basic center, basic standard deviation (sdv), basic center-sdv, chain-in-center, and chain-in-sdv connections, and we analyze a number of dynamic connections to show how opinions and their uncertainties propagate and evolve across different network structures and scenarios. We explain what insights we might gain from these mathematical results about the formation and evolution of human opinions.

Fuzzy Opinion

In this paper, the authors propose a new approach to detect opinion by SentiWorNet with the introduction of the concept of fuzzy logic. In this vein, the authors will build detection system fuzzy opinion “Fuzzy Opinion.” To give flexibility to their system, they will use a threshold of opinion. The texts are represented by a vector of word (bag of words) which will be reduced to vector the word bearer opinion by filtering with SentiWordNet. Consequently, the heart of their approach is to associate each text into two scores (bi- scoring): Sp represents the positivity of text and Sn represents the negativity of text; this is the stage of Fuzzification. To identify opinion of a text and to ensure flexibility, the authors have used a threshold of opinion. Further, they have adapted the defuzzification step for identifying opinion. Finally, they compared the results of this approach with the results of the same approach without fuzzy logic in using the same corpus.

Modeling Stock Price Dynamics With Fuzzy Opinion Networks

We propose a mathematical model for the word-of-mouth communications among stock investors through social networks and explore how the changes of the investors' social networks influence the stock price dynamics and vice versa. An investor is modeled as a Gaussian fuzzy set (a fuzzy opinion) with the center and standard deviation as inputs and the fuzzy set itself as output. Investors are connected in the following fashion: the center input of an investor is taken as the average of the neighbors' outputs, where two investors are neighbors if their fuzzy opinions are close enough to each other, and the standard deviation (uncertainty) input is taken with local, global or external reference schemes to model different scenarios of how investors define uncertainties. The centers and standard deviations of the fuzzy opinions are the expected prices and their uncertainties, respectively, that are used as inputs to the price dynamic equation. We prove that with the local reference scheme the investors converge to different groups in finite time, while with the global or external reference schemes all investors converge to a consensus within finite time and the consensus may change with time in the external reference case. We show how to model trend followers, contrarians and manipulators within this mathematical framework and prove that the biggest enemy of a manipulator is the other manipulators. We perform Monte Carlo simulations to show how the model parameters influence the price dynamics, and we apply a modified version of the model to the daily closing prices of fifteen top banking and real estate stocks in Hong Kong for the recent two years from Dec. 5, 2013 to Dec. 4, 2015 and discover that a sharp increase of the combined uncertainty is a reliable signal to predict the reversal of the current price trend.

Modeling Stock Price Dynamics with Fuzzy Opinion Networks

We propose a mathematical model for the word-of-mouth communications among stock investors through social networks and explore how the changes of the investors’ social networks influence the stock price dynamics. First, we use a Gaussian fuzzy set to model the stock price expectation of an investor, where the center and the standard deviation of the Gaussian fuzzy set represent the expected price and the uncertainty about the expected price, respectively. Then, based on a similarity measure between Gaussian fuzzy sets, we propose a bounded confidence fuzzy opinion network (BCFON) to model the social connection of investors, where only those investors whose stock price expectations are close to each other are connected, and the investors in a connected group update their fuzzy expectations as weighted averages of the previous fuzzy expectations of their neighbors. Finally, the fuzzy expectations from the BCFON are used as inputs to drive the stock price dynamics. Simulations of the price dynamic models show the details of how the topological changes of the investor networks influence the moves of the stock prices, and some common phenomena in real stock prices, such as excess volatility and trend shifting, are observed in the simulated price series and can be easily explained in our model framework. We give rigorous mathematical proofs for the convergence properties of the BCFON.

Fuzzy Opinion Networks: A Mathematical Framework for the Propagation of Opinions and Their Uncertainties Across Social Networks

Human opinions are inherently fuzzy, because the carrier of opinions is natural language where fuzzy words and vague expressions are the mainstays rather than exceptions, thus a good mathematical theory for opinion dynamics should consider the opinion and its uncertainty simultaneously.We propose a new mathematical framework for the evolution and propagation of opinions and their uncertainties, called Fuzzy Opinion Network (FON), which is the connection of a number of Gaussian Nodes, possibly through some weighted average, time-delay or logic operators, where a Gaussian Node is a Gaussian fuzzy set with the center and the standard deviation being the node inputs and the fuzzy set itself being the node output. In this framework an opinion is modeled as a Gaussian fuzzy set with the center representing the opinion itself and the standard deviation characterizing the uncertainty about the opinion. We study the basic connections of Fuzzy Opinion Networks, including basic center, basic standard deviation (sdv), basic center-sdv, chain-in-center and chain-in-sdv connections, and we analyze a number of dynamic connections, including self-feedback, compromising husband with persistent wife (or vice versa), compromising with each other, ring connection with state-dependent uncertainty, “smart” student versus “stubborn” student, and bounded confidence connection, to show how opinions and their uncertainties propagate and evolve across different network structures and scenarios.The main insights gained from the mathematical analyses of the FONs include:(a) Opinion leaders are important for a community, in the sense that the anxiety (uncertainty) of all members in the community may easily go to infinity if everyone compromises fully with all others (i.e., if there is no opinion leader in the community); when there is an opinion leader (a stubborn node in the FON), however, the anxieties of all members in the community will converge to a finite number;(b) Speed matters for time-varying FONs; for example, depending on the speed of gaining confidence through communicating with others, the uncertainties of the members in the community may stabilize at a finite number if the speed is fast or go to infinity if the speed is slow; and,(c) For state-dependent connections such as the Ring Connection or the Bounded Confidence Connection studied in this paper, a consensus will be reached if a central agent collects the opinions of all individuals and discloses them back to each individual (this shows that the media is a powerful machine to control people’s opinion), while in the decentralized control scenario (people know only the opinions of neighbors), different communities emerge where people in the same community reach a consensus but these different consensuses remain separated forever (this explains why people in the same country tend to share similar opinions while people at different countries usually have different opinions).

Preference Order of Categorized Group Opinions Considering Grade of Decision Makers

This paper proposes procedures for categorizing group opinions and making preference order of them, in circumstances of group decision-making. First, a fuzzy opinion matrix is constructed, in which each element is derived from the difference between two evaluation vectors. The difference of the two is well expressed by the symmetric evaluation method based on sigmoid function and tangent function. In fact, the conventional cosine method is not well balanced in expressing fuzzy similarity and dissimilarity simultaneously. To categorize opinions, the idea of fuzzy similarity relation is utilized. Then nature of the transitivity of opinion matrix plays an important role. Next, participants to the decision-making group are graded so that the total sum of each subgroup's dissatisfaction to the result is minimized. In this paper, using both the categorized group opinions and each grade of subgroups, final preference order is calculated. The significant feature of the proposed method appears in the combination of symmetric evaluation method, fuzzy similarity relation and optimized preference orders. By these techniques, the proposed method gives a reasonable decision-making in the context of logical treatment for various opinions due to diversified views or ideas.

Finding Opinion Strength Using Fuzzy Logic on Web Reviews

Determining the polarity and strength of opinions is an important research area over the last few years. The work challenges on opinion sentences and opinion holder extraction, opinion polarity judgment and also to measure the strength of polarity. In this paper we propose a convenient way using fuzzy techniques for analyzing opinion content in a review; our main goal is to analyze and to evaluate the sentiment in the review into a score for decision-making. The web contains product reviews and consumers are often forced to wade through many on-line reviews in order to make a product choice. We use techniques that decompose the review sentences and evaluate the individual characteristics of a product. Our task is performed in three steps: (1) mining product features that have been commented by customers; (2) identifying opinion sentences in each review and extracting the opinion phrases in each opinion sentence; (3) to measure the strength of opinion phrases to summarize the results. This paper introduces FOM (Fuzzy Opinion Miner), a supervised opinion orientation detection system that mines reviews to build a model of important product features, their evaluation by reviewers and the over all importance of the reviews.