Search Results (17)

In the context of a low-carbon economy, automotive parts supply chains face multifaceted risks, making an effective supply chain risk assessment model a crucial means of ensuring supply chain stability. Traditional evaluation methods struggle to comprehensively and accurately identify all influencing factors and their interrelationships in automotive parts supply chains. This article constructs an evaluation model based on the principle of symmetry. The “structural symmetry” is determined by the ratio of the completeness of risk dimension coverage in the indicator system to the precision of indicators, while “fusion symmetry” refers to the degree of equilibrium in information contribution during the fusion of subjective and objective weights. First, Fault Tree Analysis (FTA) and the Delphi method are adopted to establish a risk evaluation index system, whereby structural symmetry is ensured by the equilibrium between the completeness of risk dimension coverage and the accuracy of indicators in the index system. Second, drawing on the symmetric fusion principle, this study proposes a hybrid evaluation approach integrating hesitant fuzzy DEMATEL with entropy weight-coefficient of variation (HDEC), and the fusion symmetry is guaranteed by the balanced integration of subjective and objective weight information. Finally, a case study of an automotive parts supply chain enterprise quantitatively assesses and ranks risk factors, with corresponding countermeasures proposed. The symmetry-guided HDEC method achieves high accuracy, identifying indicator–causal relationships. Compared with the traditional entropy-weighted AHP algorithm, the Pearson correlation coefficient is 0.8566, and Spearman’s rank correlation coefficient is 0.88, indicating strong weight correlation and robust stability. The integration of mathematical symmetry enhances the model’s theoretical rigor, which aligns with symmetry-oriented optimization research. Full article

State evaluation of relay protection equipment constitutes a crucial component in ensuring the stable, secure, and symmetric operation of power systems. Current methodologies predominantly encompass fuzzy-rule-based control systems and data-driven machine learning approaches. The former relies on manual experience for designing fuzzy rules and membership functions and exhibits limitations in high-dimensional data integration and analysis. The latter predominantly formulates state evaluation as a classification task, which demonstrates its ineffectiveness in identifying equipment at boundary states and faces challenges in model parameter selection. To address these limitations, this paper proposes a quantitative state evaluation method for relay protection equipment based on a two-stage artificial protozoa optimizer (two-stage APO) optimized improved Conformer (two-stage APO-IConf) model. First, we modify the Conformer architecture by replacing pre-layer normalization (Pre-LN) in residual networks with post-batch normalization (post-BN) and introducing dynamic weighting coefficients to adaptively regulate the connection strengths between the first and second feed-forward network layers, thereby enhancing the capability of the model to fit relay protection state evaluation data. Subsequently, an improved APO algorithm with two-stage optimization is developed, integrating good point set initialization and elitism preservation strategies to achieve dynamic equilibrium between global exploration and local exploitation in the Conformer hyperparameter space. Experimental validation using operational data from a substation demonstrates that the proposed model achieves a RMSE of 0.5064 and a MAE of 0.2893, representing error reductions of 33.6% and 35.0% compared to the baseline Conformer, and 9.1% and 15.2% error reductions over the improved Conformer, respectively. This methodology can provide a quantitative state evaluation and guidance for developing maintenance strategies for substations. Full article

To solve the problem of state evaluations of small- and medium-span bridges such as concrete continuous girder bridges, this paper developed an extended model based on game theory. Aiming at Nash equilibrium, the combined weighting–fuzzy hierarchical comprehensive evaluation model was constructed by the combination of the analytic hierarchy process and the entropy weight method, which was corrected using the BP neural network. A three-span prestressed concrete continuous girder bridge in Wuhan was evaluated using health monitoring data and manual inspection information and compared to the results obtained using the traditional methods. The evaluation results showed that the error between the first-order frequency and the measured frequency was reduced from 17.95% to 9.00% and the bridge’s overall state score was 89.72. The evaluation model constructed by the method in this paper can take into account the contents of health monitoring and manual detection and coordinate the subjective and objective weights. Compared to the results of the analytic hierarchy process model and the fuzzy comprehensive evaluation model, the proposed model is reliable and applicable. Full article

Decision-making involves selecting the best alternative based on evaluation criteria while considering environmental impacts. The translation of environmental factors into quantifiable mathematical expressions is challenging due to the inherent uncertainties. Decision-makers can address the subjective characteristics of alternatives by incorporating fuzzy set theory into decision-making processes where uncertainty and ambiguity exist. Game theory is introduced as another approach to enhance the robustness of decision-making models, leading to more informed and flexible decision outcomes. This approach promotes strategic thinking and aids decision-making by allowing individuals to visualize the potential consequences of different decisions under various conditions. This study proposes a fuzzy multi-criteria decision support system that provides a structured framework to address the complexities of agricultural land selection. The decision support system employs a two-person zero-sum game to identify the optimal land management option, considering the strategic interactions between players. The results from the payoff matrix reveal the equilibrium point, providing an ideal solution for more effective land use planning decisions. Full article

The Yangtze River Economic Belt, as crucial component of China’s “T-shaped” strategy for territorial development and economic layout, has been challenged by the unbalanced spatial distribution of water resources, which has seriously affected high-quality development in harmony with the social economy and ecological environmental protection. In this study, we aim to enhance the conceptual definition of water resource spatial equilibrium. Additionally, we propose a water resource spatial equilibrium evaluation model based on a variable set and partial connection number. This model effectively addresses the limitations of traditional methods by incorporating fuzzy indices and dynamic information, which have previously been overlooked. The spatiotemporal characteristics and future evolutionary trend of water resource spatial equilibrium were analyzed in 11 provinces and 110 cities in the Yangtze River Economic Belt from 1999 to 2018. The results showed that the conceptual definition of water resource spatial equilibrium involves the water resource endowment, water resource development, water resource utilization, water resource supply and demand, water resource matching, and water resource protection. The water resource spatial equilibrium in the 11 provinces gradually improved following a temporal trend; in terms of the spatial trend, the south was better than the north and the west was better than the east. These provinces were sorted as follows: Yunnan > Sichuan > Zhejiang > Jiangxi > Hunan Province > Guizhou > Hubei > Chongqing > Anhui > Jiangsu > Shanghai. The evolutionary trend increased except in Yunnan. The water resource spatial equilibrium of the 110 cities showed that the spatial trends of the three major urban agglomerations were much better than in the other regions, and the temporal trend steadily improved. The 11 provinces and 110 cities could be divided into three and five categories, respectively, according to their spatiotemporal trends. City-scale research on water resource spatial equilibrium can effectively identify and optimize the control area compared with using a provincial scale. When the control targets were set to 20%, 40%, 60%, and 80%, the proportion of the administrative area based on the city scale decreased by 1.20%, 4.99%, 10.52%, and 19.05%, respectively. Full article

The primary objective of this paper is to expand Jamshidian’s bond option formula and compatible one-factor term structure models by incorporating the existence of uncertainty in the parameters governing interest-rate fluctuations. Specifically, we consider imprecision in the parameters related to the speed of reversion, equilibrium short-term interest rate, and volatility. To model this uncertainty, we utilize fuzzy numbers, which, in this context, are interpreted as epistemic fuzzy sets. The second objective of this study is to propose a methodology for estimating these parameters based on historical data. To do so, we use the possibility distribution functions capability to quantify imprecise probability distributions. Furthermore, this paper presents an application to the term structure of fixed-income bonds with the highest credit rating in the Euro area. This empirical application allows for evaluating the effectiveness of the fuzzy extension in fitting the dynamics of interest rates and assessing the suitability of the proposed extension. Full article

Image segmentation has been one of the most active research areas in the last decade. The traditional multi-level thresholding techniques are effective for bi-level thresholding because of their resilience, simplicity, accuracy, and low convergence time, but these traditional techniques are not effective in determining the optimal multi-level thresholding for image segmentation. Therefore, an efficient version of the search and rescue optimization algorithm (SAR) based on opposition-based learning (OBL) is proposed in this paper to segment blood-cell images and solve problems of multi-level thresholding. The SAR algorithm is one of the most popular meta-heuristic algorithms (MHs) that mimics humans’ exploration behavior during search and rescue operations. The SAR algorithm, which utilizes the OBL technique to enhance the algorithm’s ability to jump out of the local optimum and enhance its search efficiency, is termed mSAR. A set of experiments is applied to evaluate the performance of mSAR, solve the problem of multi-level thresholding for image segmentation, and demonstrate the impact of combining the OBL technique with the original SAR for improving solution quality and accelerating convergence speed. The effectiveness of the proposed mSAR is evaluated against other competing algorithms, including the L’evy flight distribution (LFD), Harris hawks optimization (HHO), sine cosine algorithm (SCA), equilibrium optimizer (EO), gravitational search algorithm (GSA), arithmetic optimization algorithm (AOA), and the original SAR. Furthermore, a set of experiments for multi-level thresholding image segmentation is performed to prove the superiority of the proposed mSAR using fuzzy entropy and the Otsu method as two objective functions over a set of benchmark images with different numbers of thresholds based on a set of evaluation matrices. Finally, analysis of the experiments’ outcomes indicates that the mSAR algorithm is highly efficient in terms of the quality of the segmented image and feature conservation, compared with the other competing algorithms. Full article

As a result of the continuous improvement in passengers’ requirements for the quality of train operation, the comfort of high-speed train operation has been paid increasing attention. The evaluation of comfort has gradually changed from the narrow sense of a comfort evaluation model containing only vibration to the generalized evaluation of passengers’ overall satisfaction with the ride environment of specific lines. The factors affecting comfort evaluation include physical, physiological, and psychological aspects. To address the problems that the existing comfort evaluation model has a single index and that the weight determination of some indicators is greatly affected by subjectivity, we built a high-speed train comfort evaluation model based on variable weight theory. Combined with the actual working conditions of the Baolan passenger dedicated line, dynamic detection data and noise monitoring data captured by a track inspection car were combined with a passenger ride comfort questionnaire survey. In addition, the initial weight value of each factor was optimized by constructing an equilibrium function to realize the balance between the various factors, so as to realize the comprehensive fuzzy evaluation of high-speed train comfort. The results show that the comprehensive evaluation result of the comfort degree of the high-speed train on the Tongwei to Lanzhou section of the Baolan passenger dedicated line has a grade of II. The fuzzy scores of the evaluations using variable weights and constant weights were analyzed from the perspective of membership degree. The variable weight optimization avoids the one-sidedness and extremeness of the constant weight calculation. The comprehensive evaluation results are closer to the real situation. The research results can provide a reference for the comfort evaluation of high-speed trains with extreme differences in state values and constant weights and help in the acquisition of more realistic evaluation results. Full article

Increasing consumption of energy calls for proper approximation of demand towards a sustainable and cost-effective development. In this work, novel hybrid methodologies aim to predict the annual thermal energy demand (ATED) by analyzing the characteristics of the building, such as transmission coefficients of the elements, glazing, and air-change conditions. For this objective, an adaptive neuro-fuzzy-inference system (ANFIS) was optimized with equilibrium optimization (EO) and Harris hawks optimization (HHO) to provide a globally optimum training. Moreover, these algorithms were compared to two benchmark techniques, namely grey wolf optimizer (GWO) and slap swarm algorithm (SSA). The performance of the designed hybrids was evaluated using different accuracy indicators, and based on the results, ANFIS-EO and ANFIS-HHO (with respective RMSEs equal to 6.43 and 6.90 kWh·m⁻²·year⁻¹ versus 9.01 kWh·m⁻²·year⁻¹ for ANFIS-GWO and 11.80 kWh·m⁻²·year⁻¹ for ANFIS-SSA) presented the most accurate analysis of the ATED. Hence, these models are recommended for practical usages, i.e., the early estimations of ATED, leading to a more efficient design of buildings. Full article

The linear parameter-varying (LPV) models have broad applications in advanced mathematics and modern control systems. This paper introduces a new method for controlling the LPV systems. This method includes the gain-scheduled state-feedback technique and a fuzzy system to calculate the state-feedback gain. The main goal of the control system is to stabilize the system and bring its states to equilibrium points. Linear matrix inequalities calculate feedback gains to stabilize the system. On the other hand, a fuzzy control system also produces a combined signal with the primary controller signal to speed up this operation. Lyapunov’s theory is used to guarantee the control system’s stability. Finally, to evaluate the performance of the proposed control system, the inverted pendulum has been investigated as a case study. The results show that the proposed method has good efficiency and performance. Full article

With the ever-increasing demand for air traffic over the years, safety risk assessment has become significant in maintaining the operational safety of the air transport system for long-term development towards sustainability. This paper conducts a safety risk assessment of the air traffic control (ATC) system based on game theory and cloud matter element analysis. The safety risk of the ATC system is evaluated from four aspects, including human, machine, environment, and management. The Nash equilibrium is introduced from game theory to weigh the indicators. The cloud matter element assessment adopts the cloud model from fuzzy sets and probability theory to replace the certain value in conventional matter element theory, which takes the randomness, ambiguity, and incompatibility of the indicators into consideration. In this sense, the safety risk level of the ATC system can be evaluated by calculating the correlation degree of the standard cloud matter element between the indicators and the risks. This paper expands the research scope by introducing and combing game theory and cloud matter element analysis. Furthermore, the applicability and the robustness of the method are examined with a case study of the ATC system, which enriches the existing literature and points out the direction for future work. Full article

In this paper, the problem of controlling a human-like bipedal robot while walking is studied. The control method commonly applied when controlling robots in general and bipedal robots in particular, was based on a dynamical model. This led to the need to accurately define the dynamical model of the robot. The activities of bipedal robots to replace humans, serve humans, or interact with humans are diverse and ever-changing. Accurate determination of the dynamical model of the robot is difficult because it is difficult to fully and accurately determine the dynamical quantities in the differential equations of motion of the robot. Additionally, another difficulty is that because the robot’s operation is always changing, the dynamical quantities also change. There have been a number of works applying fuzzy logic-based controllers and neural networks to control bipedal robots. These methods can overcome to some extent the uncertainties mentioned above. However, it is a challenge to build appropriate rule systems that ensure the control quality as well as the controller’s ability to perform easily and flexibly. In this paper, a method for building a fuzzy rule system suitable for bipedal robot control is proposed. The design of the motion trajectory for the robot according to the human gait and the analysis of dynamical factors affecting the equilibrium condition and the tracking trajectory were performed to provide informational data as well as parameters. Based on that, a fuzzy rule system and fuzzy controller was proposed and built, allowing a determination of the control force/moment without relying on the dynamical model of the robot. For evaluation, an exact controller based on the assumption of an accurate dynamical model, which was a two-feedback loop controller based on integrated inverse dynamics with proportional integral derivative, is also proposed. To confirm the validity of the proposed fuzzy rule system and fuzzy controller, computation and numerical simulation were performed for both types of controllers. Comparison of numerical simulation results showed that the fuzzy rule system and the fuzzy controller worked well. The proposed fuzzy rule system is simple and easy to apply. Full article

With the rapid development of urbanization, substantial land areas and houses are expropriated, which can cause huge numbers of disputes related to expropriation compensation. The root of the disputes is that the associated subjects are affected by various behavioral preferences and make different cognitive fairness judgments based on the same compensation price. However, the existing expropriation compensation strategies based on the market value under the assumption of “the economic man” hypothesis cannot meet the fairness preference demands of the expropriated. Therefore, finding a compensation price that satisfies subjects’ multidimensional fairness preferences, including profit-seeking, loss aversion, and interactive fairness preferences, is necessary. Only in this way can the subjects reach an agreement regarding fair compensation and resolve their disputes. Because of the fuzziness of subjects’ expected revenues, this paper innovatively introduces trigonometric intuitional fuzzy numbers to construct one-dimensional and multidimensional fair fuzzy equilibrium evaluation models. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is adopted to convert a multidimensional problem into a multiattribute group decision problem, which simplifies the problem of finding multidimensional equilibrium when considering the multidimensional fairness preferences of the two subjects. Real case data are introduced to verify the validity of this method. The research results show that upward revision of the multidimensional fairness preferences based on the market value assists in achieving a fair compensation agreement. Consideration of the influence of the subjects’ multidimensional fairness preferences on the fairness equilibrium is conducive to resolving the disputes, and provides a reference for the settlement of expropriation compensation disputes in developing countries. Full article

For energy-limited wireless sensor networks (WSNs), we propose a multiparameter fusion decision routing (MPFDR) algorithm in this study. This algorithm gives a comprehensive account of the residual energy and forward distance, single-hop transmission ratio, cache queue, and energy equilibrium degree. It calculates the routing evaluation parameters of the forward neighbors, realizing a multidirectional reflection of the network status. Simultaneously, combined with the defined routing selection strategy based on the parameter contribution degree and fuzzy contribution degree, the fusion contribution degree of each forward neighbor is obtained. Then, the node with the most considerable fusion contribution degree is selected as the next hop. Finally, the performance of the MPFDR algorithm is simulated and compared with other algorithms. Simulation results indicate that our algorithm has good congestion control ability in energy-limited wireless sensor networks and can significantly reduce the packet loss rate and average hops. Full article

Demand Response Management (DRM) is considered one of the crucial aspects of the smart grid as it helps to lessen the production cost of electricity and utility bills. DRM becomes a fascinating research area when numerous utility companies are involved and their announced prices reflect consumer’s behavior. This paper discusses a Stackelberg game plan between consumers and utility companies for efficient energy management. For this purpose, analytical consequences (unique solution) for the Stackelberg equilibrium are derived. Besides this, this paper presents a distributed algorithm which converges for consumers and utilities. Moreover, different power consumption activities on the basis of time series are becoming a basic need for load prediction in smart grid. Load forecasting is taken as the significant concerns in the power systems and energy management with growing technology. The better precision of load forecasting minimizes the operational costs and enhances the scheduling of the power system. The literature has discussed different techniques for demand load forecasting like neural networks, fuzzy methods, Naïve Bayes, and regression based techniques. This paper presents a novel knowledge based system for short-term load forecasting. The algorithms of Affinity Propagation and Binary Firefly Algorithm are integrated in knowledge based system. Besides, the proposed system has minimum operational time as compared to other techniques used in the paper. Moreover, the precision of the proposed model is improved by a different priority index to select similar days. The similarity in climate and date proximity are considered all together in this index. Furthermore, the whole system is distributed in sub-systems (regions) to measure the consequences of temperature. Additionally, the predicted load of the entire system is evaluated by the combination of all predicted outcomes from all regions. The paper employs the proposed knowledge based system on real time data. The proposed scheme is compared with Deep Belief Network and Fuzzy Local Linear Model Tree in terms of accuracy and operational cost. In addition, the presented system outperforms other techniques used in the paper and also decreases the Mean Absolute Percentage Error (MAPE) on a yearly basis. Furthermore, the novel knowledge based system gives more efficient outcomes for demand load forecasting. Full article