Search Results (1,680)

This paper discusses the course tracking control of unmanned surface vehicles with actuator input quantization and an event-triggered mechanism. The system control laws are designed based on the backstepping method, combining dynamic surface control technology to mitigate the computational complexity expansion of virtual control laws. A fuzzy logic system can be used to approximate the uncertainties in the control system. The control system’s control inputs are quantized by using uniform quantizers. Then, the event-triggered adaptive fuzzy quantization control method is introduced, which can reduce the frequency of control actions and effectively reduce the communication burden. The stability of the control system is rigorously proven using Lyapunov stability theory, ensuring that all signals in the closed-loop system remain bounded. Finally, simulation tests are used to show the algorithm’s efficiency and usefulness. Full article

Interdisciplinary teaching is a pivotal strategy for deepening disciplinary theory and broadening students’ cognitive boundaries, crucial for the sustainability of education. By considering scientific knowledge’s humanistic background and technological evolution, this study proposes a novel interdisciplinary teaching framework based on the Source–Knowledge–Use (SKU) paradigm. Then, taking fuzzy mathematics as a case, the Humanities–Science–Technology Model (HSTM), based on a tripartite progression from humanistic foundations to scientific principles and then to technological applications, was established. This study systematically expounds the HSTM’s framework, contents, and implementation design, while critically examining potential challenges and corresponding mitigation strategies. The proposed SKU-based interdisciplinary teaching framework not only provides methodological guidance for interdisciplinary instruction in fuzzy mathematics but also offers transferable insights for cognate disciplines seeking to implement sustainable educational practices. Full article

At present, the high complexity of the environment, the uncertainty of income, and the choice of strategies have attracted extensive attention from all walks of life who are committed to studying the game of collaborative innovation between government and industry–university–research. Based on this, first of all, with the help of stochastic evolutionary game theory and fuzzy theory, this paper constructs a multi-party stochastic evolutionary game model of green technology innovation about the government guidelines and the joint promotion of industry, universities, and research institutes. Secondly, it discusses the evolution law of behavior strategies of each game subject and the main factors to maintain the alliance’s stability under fuzzy income. The numerical simulation results show the following: (1) Reputation gains have a significant positive correlation with the evolution stability of alliance behavior, and the incorporation of reputation gains or losses will effectively maintain the cooperation stability of the alliance. (2) Under the influence of product greenness, government subsidies, and long-term benefits, it will promote the pace consistency of cooperative decision-making between industry, universities, and research institutes, and accelerate the evolution of alliances. (3) The enterprise’s ability and the research party’s ability will restrict each other. When one party’s ability is low, its willingness to choose a cooperation strategy may be slightly low due to technology spillover and other reasons. When the two parties’ abilities match, their behavior strategies will increase their willingness to cooperate with their abilities. Compared with the traditional evolutionary game, this study fully considers the uncertainty of the environment and provides theoretical support and practical guidance for the high-quality development strategy of the industry–university–research green technology innovation alliance. Full article

This article delves into the problem of fuzzy adaptive event-triggered (ET) formation control for nonholonomic mobile robots (NMRs) subject to full-state constraints. Fuzzy logic systems (FLSs) are employed to identify the unknown nonlinear functions within the system. To guarantee that all system states remain within their constraint boundaries, barrier Lyapunov functions (BLFs) are meticulously constructed. Subsequently, within the framework of the backstepping control design algorithm, we propose a novel fuzzy adaptive ET formation controller. Our ET mechanism can achieve an overall resource-saving rate of 88.17% for the four robots. Rigorous theoretical analysis demonstrates that the designed strategy not only ensures the stability of the controlled NMRs but also enables the formation tracking errors to converge to a small neighborhood around zero. Notably, the BLFs-based control approach presented herein endows the system with the capacity to avoid collisions to a certain degree, enhancing the overall safety and reliability of the robot formation. Finally, a simulation example is provided. The results vividly illustrate the effectiveness and practicality of the proposed theory, validating its potential for real-world applications in the field of nonholonomic mobile robot formation control. Full article

Tourist perceived value is an important antecedent to loyalty by enhancing satisfaction, revisiting intentions, and recommendations, thereby promoting sustainable development of forest parks. However, existing research has not sufficiently examined the configurations of perceived value in relation to increasing tourist loyalty specifically in the context of forest parks, representing a notable gap in the existing literature that requires further investigation. To address this gap, both covariance-based structural equation model (CB-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) models were conducted to explore the joint effects of perceived value on tourist loyalty and identify pathways of perceived value dimensions to increase tourist loyalty, based on the Value-Satisfaction-Loyalty Chain model. A total of 404 valid questionnaires were collected from 436 visitors to the Yingde National Forest Park in southern China. Among the respondents, 54.2% were male, nearly 50% were over 36 years old, and 60% held a university degree. The results indicate that perceived value significantly influences tourist loyalty, with satisfaction playing a crucial mediating role between perceived value and loyalty. Notably, the indirect effect mediated by satisfaction was found to be greater than the direct effect of perceived value on loyalty. Five distinct pathways were identified for enhancing tourist loyalty, categorized into three models: the economic value-driven model, the functional value and epistemic value dual-core driven model, and the emotional and social value dual-core driven model. Additionally, four pathways were identified for enhancing tourist satisfaction, which subsequently improves tourist loyalty. These four pathways were grouped into two modes: the economic value-driven model and the functional value plus driven model. This study introduces an innovative perspective on the relationship between tourist perceived value and loyalty in forest parks, identifying key factors and configurations within the five dimensions of perceived value that enhance both tourist loyalty and satisfaction. Moreover, it extends the application of the Value-Satisfaction-Loyalty Chain theory to a forest park context. The findings provide valuable insights for forest park managers, guiding them in enhancing perceived value through targeted pathways to increase tourist revisit intentions and recommendations, ultimately supporting the park’s sustainable development. The influence of individual items on tourist satisfaction and loyalty, along with the identification of optimal item combinations to enhance loyalty, necessitates further investigation. Furthermore, a deeper exploration of the heterogeneity of factors and pathways for improving tourist loyalty is required. Full article

This research tackles an essential gap in understanding how passengers prefer to interact with artificial intelligence (AI) or human agents in airline customer service contexts. Using a mixed-methods approach that combines statistical analysis with fuzzy set theory, we examine these preferences across a range of service scenarios. With data from 163 participants’ Likert scale responses, our qualitative analysis via fuzzy set methods complements the quantitative results from regression analyses, highlighting a preference model contingent on context: passengers prefer AI for straightforward, routine transactions but lean towards human agents for nuanced, emotionally complex issues. Our regression findings indicate that perceived benefits and simplicity of tasks significantly boost satisfaction and trust in AI services. Through fuzzy set analysis, we uncover a gradient of preference rather than a stark dichotomy between AI and human interaction. This insight enables airlines to strategically implement AI for handling routine tasks while employing human agents for more complex interactions, potentially improving passenger retention and service cost-efficiency. This research not only enriches the theoretical discourse on human–computer interaction in service delivery but also guides practical implementation with implications for AI-driven services across industries focused on customer experience. Full article

Considering the characteristics of power quality signals and denoising requirements, a denoising method of a power quality signal based on parameter coordination of membership function in fuzzy logic theory is proposed in this paper. First of all, for the signal sequence of power quality, seven masks are designed to make the best use of the signal sequence information. Secondly, based on fuzzy logic theory, the corresponding membership degrees are calculated for these seven masks, and the average value of all points in these masks is used as the input of fuzzy logic theory. Then, according to the membership function of the input and output variable, the boundary parameters are designed harmoniously to obtain the best denoising effect. Finally, based on the experimental simulation results, it is proved that the proposed method can not only smooth the noise well, but also keep the information for the abrupt point in its entirety, which is more suitable for denoising power quality signals compared with the traditional filtering method. Full article

In this paper, we investigate the evolution characteristics of floor failure during pressured mining in extra-thick coal seams. A mechanical expression relating floor failure depth to seam thickness is established based on soil mechanics and mine pressure theory. The findings reveal a linear relationship between seam thickness and floor failure depth; specifically, as the coal seam thickens, the depth of floor failure increases. To simulate the mining process of extra-thick coal seams, FLAC3D numerical simulation software is utilized. We analyze the failure process, failure depth, and the behavior of water barriers at the coal seam floor under the influence of extra-thick coal seam mining from three perspectives: rock displacement evolution in the floor, stress evolution in the floor, and plastic deformation. Based on geological characteristics observed in the Longwanggou mine field, we establish a main control index system for assessing floor water-inrush risk. This system comprises 11 primary control factors: water abundance, permeability, water pressure, complexity of geological structure, structural intersection points, thickness of both actual and equivalent water barriers, thickness ratio of brittle–plastic rocks to coal seams, as well as depths related to both coal seams and instances of floor failure. Furthermore, drawing upon grey system theory and fuzzy mathematics within uncertainty mathematics frameworks leads us to propose an innovative approach—the interval grey optimal clustering model—designed specifically for risk assessment concerning potential floor water inrush during pressured mining operations involving extra-thick coal seams. This method of mine water inrush risk assessment is applicable for popularization and implementation in mines with analogous conditions, and it holds practical significance for the prevention of mine water damage. Full article

Split air conditioning units are crucial for ensuring the thermal comfort of buildings. Conventional scheduling or pre-timed system activities result in high consumption and wasted energy. This study proposes an intelligent control system for automatic setpoint adjustment in an educational building based on real-time indoor and outdoor environmental and room occupancy data. Principal component analysis was used to identify energy consumption components in ramp-up and steady-state power usage scenarios. K-means clustering was then used to categorize environmental scenarios and occupancy patterns to identify operational states, predict power consumption and environmental variables, and generate fuzzy inference system rules. The application of rough set theory eliminated rule redundancy by at least 99.27% and enhanced computational speed by 96.40%. After testing using real historical data from an uncontrolled environment and occupant thermal comfort satisfaction surveys reflecting a range of ACU setpoints, the enhanced inference system achieved daily average power savings of 25.56% and a steady-state power period at 63.24% of the ACU operating time, as compared to conventional and variable setpoint operations. The proposed technique provides a basis for dynamic and data-driven decision-making, enabling sustainable energy management in smart building applications. Full article

In wireless sensor networks, sensors often collect and transmit a large amount of redundant data, which can lead to excessive battery consumption and subsequent performance degradation. To solve this problem, this paper proposes a Zoom-In Zoom-Out (ZIZO) method based on sparse vectors (SV-ZIZO). It operates in two parts: At the sensor level, given the temporal similarity of the data, a new compression method based on the sparse vector representation of segmented regions is proposed. This method can not only effectively ensure the compression ratio but also improve the accuracy of data restoration. At the cluster-head (CH) level, by utilizing the spatial similarity of the data, the fuzzy clustering theory is introduced to put some sensors into hibernation mode, thereby reducing data transmission. Meanwhile, the sampling frequency of the sensors is dynamically adjusted by calculating the redundancy rate of the collected periodic data. The experimental results show that compared with other existing methods, the algorithm proposed in this paper increases the data compression ratio by 21.8% and can reduce energy consumption by up to 95%. Full article

In many real-world situations, we deal with data that exhibit both randomness and vagueness. To manage such uncertain information, fuzzy theory provides a useful framework. Specifically, to explore causal relationships in these datasets, a lot of fuzzy regression models have been introduced. However, while fuzzy regression analysis focuses on estimation, it is equally important to study the mathematical characteristics of fuzzy regression estimates. Despite the statistical significance of optimal properties in large-sample scenarios, only limited research has addressed these topics. This study establishes key optimal properties, such as strong consistency and asymptotic normality, for the fuzzy least-squares estimator (FLSE) in general linear regression models involving fuzzy input–output data and random errors. To achieve this, fuzzy analogues of traditional normal equations and FLSEs are derived using a suitable fuzzy metric. Additionally, a confidence region based on FLSEs is proposed to facilitate inference. The asymptotic relative efficiency of FLSEs, compared to conventional least-squares estimators, is also analyzed to highlight the efficiency of the proposed estimators. Full article

This study investigates the determinants of superior long-term business performance in Thai digital entrepreneurship through an innovative mixed-method approach combining Rough Set Fuzzy Theory and Second-order Confirmatory Factor Analysis. This research addresses a significant gap in the existing literature by incorporating business strategies, product innovation, social media adoption, and entrepreneurial orientation into a comprehensive framework, extending beyond traditional Technology–Organization–Environment (TOE) models. This study analyzes seven key factors that influence digital business success: technology, organization, external environment, social media adoption, business strategy, product innovation, and entrepreneurial orientation. The methodological approach employed for this study utilized expert consensus validation and model verification techniques to develop a novel integrated model specifically tailored for Thailand’s digital SME context. The findings reveal that business strategy and entrepreneurial orientation are primary drivers of business success. This research provides valuable insights for practitioners in the Thai digital entrepreneurship ecosystem, offering a structured approach to achieving sustainable long-term business success. Full article

This paper addresses the challenge of assessing photovoltaic (PV) hosting capacity in distribution networks while accounting for the uncertainty of PV output, a critical step toward achieving sustainable energy transitions. Traditional optimization methods for dealing with uncertainty, including robust optimization (RO) and stochastic optimization (SO), often result in overly conservative or optimistic assessments, hindering the efficient integration of renewable energy. To overcome these limitations, this paper proposes a novel distributionally robust chance-constrained (DRCC) assessment method based on Kullback–Leibler (KL) divergence. First, the time-segment adaptive bandwidth kernel density estimation (KDE) combined with Copula theory is employed to model the conditional probability density of PV forecasting errors, capturing temporal and output-dependent correlations. The KL divergence is then used to construct a fuzzy set for PV output, quantifying its uncertainty within specified confidence levels. Finally, the assessment results are derived by integrating the fuzzy set into the optimization model. Case studies demonstrate its effectiveness of the method. Key findings indicate that higher confidence levels reduce PV hosting capacities due to broader uncertainty ranges, while increased historical sample sizes enhance the accuracy of distribution estimates, thereby increasing assessed capacities. By balancing conservatism and optimism, this method enables safer and more efficient PV integration, directly supporting sustainability goals such as reducing fossil fuel dependence and lowering carbon emissions. The findings provide actionable insights for grid operators to maximize renewable energy utilization while maintaining grid stability, advancing global efforts toward sustainable energy infrastructure. Full article

As a new type of rail transit vehicle, maglev trains have extremely high requirements for safety and reliability. With the gradual commercial operation of maglev trains, how to scientifically and effectively assess the safety and analyze the risks of train equipment has become an urgent issue to be addressed. Against the backdrop of the practical application of maglev train projects, this paper integrates domestic and international risk analysis models, proposes the steps for conducting the risk analysis of maglev rail transit, and establishes a risk analysis system for the entire lifecycle of maglev rail transit. Based on the results of fault analysis, a risk analysis of the levitation system is carried out. The theory of multi-attribute decision-making is studied, new risk evaluation indicators are established using triangular fuzzy numbers, the risk levels of the levitation system are determined, and the weak links within the system and the relationships between the pieces of equipment are identified. These efforts provide guidance for enhancing the safety and reliability of train equipment and for carrying out train maintenance work. Full article

In recent years, the disruption of the prefabricated building supply chain has led to increased construction period delays and cost overruns, limiting the development and popularization of prefabricated buildings in China. Therefore, this study established a vulnerability evaluation index system for the prefabricated building supply chain using the driving force–pressure–state–impact–response (DPSIR) framework. We employed the intuitionistic fuzzy analytic hierarchy process (IFAHP), the projection pursuit (PP) model, and variable weight theory to determine the indicator weights. The IFAHP was utilized to reduce the subjectivity in weight assignment and to obtain the degree of membership, non-membership, and hesitation of experts in evaluating the importance of indicators. The PP model was used to determine objective weights based on the structure of the evaluation data, and variable weight theory was applied to integrate subjective and objective weights according to management needs. We utilized Set Pair Analysis (SPA) to establish a vulnerability evaluation model for the building supply chain, treating evaluation data and evaluation levels as a set pair. By analyzing the degree of identity, difference, and opposition of the set pair, we assessed and predicted the vulnerability of the building supply chain. Taking the Taohua Shantytown project in Nanchang as a case study, the results showed that the primary index with the greatest influence on the vulnerability of the prefabricated building supply chain was the driving force, with a weight of 0.2692, followed by the secondary indices of market demand and policy support, with weights of 0.0753 and 0.0719, respectively. The project’s average vulnerability rating was moderate (Level III), and it showed an improvement trend. During the project’s implementation, the total cost overrun of the prefabricated building supply chain was controlled within 5% of the budget, the construction period delay did not exceed 7% of the plan, and the rate of production safety accidents was below the industry average. The results demonstrated that the vulnerability assessment method for the prefabricated building supply chain based on SPA comprehensively and objectively reflected the vulnerability of the supply chain. It is suggested to improve the transparency and flexibility of the supply chain, strengthen daily management within the supply chain, and enhance collaboration with supply chain partners to reduce vulnerability. Full article