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One of the keys in time-dependent routing is determining the weight of each road network link based on traffic information. To facilitate the estimation of the road's weight, Global Position System (GPS) data are commonly used in obtaining real-time traffic information. However, the information obtained by taxi-GPS does not cover the entire road network. Aiming at incomplete traffic information on urban roads, this paper proposes a novel fuzzy inference method. It considers the combined effect of road grade, traffic information, and other spatial factors. Taking the third law of geography as the basic premise, that is, the more similar the geographical environment, the more similar the characteristics of the geographical target will be. This method uses a Typical Link Pattern (TLP) model to describe the geographical environment. The TLP represents typical road sections with complete information. Then, it determines the relationship between roads lacking traffic information and the TLPs according to their related factors. After obtaining the TLPs, this method ascertains the weight of road links by calculating their similarities with TLPs based on the theory of fuzzy inference. Aiming at road links at different places, the dividing – conquering strategy and globe algorithm are also introduced to calculate the weight. These two strategies are used to address the excessively fragmented or lengthy links. The experimental results with the case of Newcastle show robustness in that the average Root Mean Square Error (RMSE) is 1.430 mph, and the bias is 0.2%; the overall RMSE is 11.067 mph, and the bias is 0.6%. This article is the first to combine the third law of geography with fuzzy inference, which significantly improves the estimation accuracy of road weights with incomplete information. Empirical application and validation show that the method can accurately predict vehicle speed under incomplete information. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Inference using fuzzy rules enables decision-making that is supported with imprecise knowledge. Unlike conventional fuzzy reasoning approaches which directly perform pattern-matching in response to an input observation, recent techniques have integrated rule-firing-based and rule interpolating-based inference methods. This is in order to address challenging issues where observations are of different matching degrees to the rules within a given rule base, including unmatched ones. While applied generally, such a unified inference mechanism may become too complex to exploit the entire rule base for deriving a reasonable conclusion. In practice, only a small number of 'appropriate' rules are selected to accomplish the required inference. This paper presents an enhanced integrated fuzzy inference mechanism, which is fed with fewer rules returned by a weight-guided selection procedure. In particular, the weights of rule attributes are utilised in a dual manner: guiding the selection of appropriate rules for rule firing and determining the nearest neighbouring rules for rule interpolation. The resulting mechanism is applied to a real-world problem, empirically demonstrating its significant efficacy. [ABSTRACT FROM AUTHOR]

As significant vehicles for applying fuzzy set theories, fuzzy inference systems (FISs) have been widely utilized in artificial intelligence. However, challenges such as computational complexity and subjective design persist in FIS implementation. To address these issues, this paper introduces the fuzzy inference system based on axiomatic fuzzy sets (FIS-AFSs), which includes a fuzzy rule base and a fuzzy inference engine. This system eliminates the need for subjective decisions in the selection of fuzzification and defuzzification methods. The theoretical foundation of the approach involves defining the multi-dimensional vague partition (VP) of the multi-dimensional universe using an overlap function to aggregate one-dimensional VPs. Additionally, an axiomatic fuzzy set (AFS) on the multi-dimensional universe is defined. Building on this foundation, algorithms for single-input single-output (SISO) and multi-input single-output (MISO) fuzzy inference are developed using AFSs, eliminating the need for subjective fuzzy implication operators. The FIS-AFSs, with its universal approximation property and theoretical approximation precision, are then analyzed. Experimental tests are conducted to evaluate the approximation capabilities of the FIS-AFSs. Results from both theoretical analysis and experimental testing demonstrate that FIS-AFSs can achieve high approximation precision. [ABSTRACT FROM AUTHOR]

In order to solve the problem of accurate and low-cost location of substation personnel in digital cities, a substation personnel location system based on wireless sensor cloud computing network is proposed. ZigBee wireless sensor cloud computing network is introduced into the substation to improve the direct location algorithm of substation personnel, and a fuzzy reasoning algorithm is proposed. The algorithm takes the signal strength received by each reference node and the relative distance between the reference nodes as input. After fuzzy, fuzzy reasoning and deblurring, the reliability of the received signal strength of each reference node is obtained, and then three reference nodes with high reliability are selected for trilateral positioning calculation. The experimental results show that the positioning error after improvement is more stable than that before improvement, and the maximum error before improvement is 1.4115m. Practice has proved that the algorithm can significantly improve the positioning accuracy of substation personnel without adding any hardware and using fewer nodes. [ABSTRACT FROM AUTHOR]

The postweaning phase is stressful for rabbits due to maternal separation and the introduction of solid feed. Stress can be aggravated when animals are subjected to thermal discomfort. Therefore, thermal variables, such as air temperature, which influence the productive and physiological performance of animals, require greater control in this phase of life of rabbits to ensure their well-being and productive efficiency. Thus, the objective of the present study was to develop and compare fuzzy inference systems (FISs) with different configurations to predict the feed intake (FI) of New Zealand White (NZW) rabbits subjected to different thermal conditions after weaning. The experiment lasted 14 days, and twelve rabbits between 30 and 43 days old were used. The animals were housed in air-conditioned wind tunnels and subjected to air temperatures of 20, 24, 28 and 32 °C. For the FIS configurations, Mamdani inference with five defuzzification methods (center of gravity (COG), bisector of area (BOA), largest of maximum (LOM), mean of maximum (MOM) and smallest of maximum (SOM)) and Sugeno inference with two defuzzification methods (weighted average (WA) and weighted sum (WS)), were evaluated. In both inference methods, the input variables (air temperature and time after weaning) were represented by triangular, Gaussian or trapezoidal functions. In turn, the output variable (FI, g) was represented by triangular, Gaussian or trapezoidal functions in the Mamdani FIS and by singleton functions in the Sugeno FIS. Thus, all developed FISs were validated, and their results were compared to the experimental data using statistical indices. As a result, adequate FI prediction performances were obtained for rabbits using both inference methods, regardless of the configurations used in their development. However, the smallest simulation errors were obtained using the Sugeno FIS with Gaussian inputs and WA defuzzification and is therefore a system with greater generalization capacity for unknown scenarios. Thus, the developed models can be used as a support system for decisions on the management of rabbits, aiding the efficient production and welfare of the animals, as well as the maintenance of thermal variables through the activation of installed climate systems inside the rabbit production environment. Trial registration number: 085/17. Date of registration: 14/12/2017. [ABSTRACT FROM AUTHOR]

In recent years, cybersecurity management has increasingly required advanced methodologies capable of handling complex, evolving threat landscapes. Scenario network-based approaches have emerged as effective strategies for managing uncertainty and adaptability in cybersecurity projects. This article introduces a scenario network-based approach for managing cybersecurity projects, utilizing fuzzy linguistic models and a Takagi–Sugeno–Kanga fuzzy neural network. Drawing upon L. Zadeh's theory of linguistic variables, the methodology integrates expert analysis, linguistic variables, and a continuous genetic algorithm to predict membership function parameters. Fuzzy production rules are employed for decision-making, while the Mamdani fuzzy inference algorithm enhances interpretability. This approach enables multi-scenario planning and adaptability across multi-stage cybersecurity projects. Preliminary results from a research prototype of an intelligent expert system—designed to analyze project stages and adaptively construct project trajectories—suggest the proposed approach is effective. In computational experiments, the use of fuzzy procedures resulted in an over 25% reduction in errors compared to traditional methods, particularly in adjusting project scenarios from pessimistic to baseline projections. While promising, this approach requires further testing across diverse cybersecurity contexts. Future studies will aim to refine scenario adaptation and optimize system response in high-risk project environments. [ABSTRACT FROM AUTHOR]

One of the keys in time-dependent routing is determining the weight of each road network link based on traffic information. To facilitate the estimation of the road’s weight, Global Position System (GPS) data are commonly used in obtaining real-time traffic information. However, the information obtained by taxi-GPS does not cover the entire road network. Aiming at incomplete traffic information on urban roads, this paper proposes a novel fuzzy inference method. It considers the combined effect of road grade, traffic information, and other spatial factors. Taking the third law of geography as the basic premise, that is, the more similar the geographical environment, the more similar the characteristics of the geographical target will be. This method uses a Typical Link Pattern (TLP) model to describe the geographical environment. The TLP represents typical road sections with complete information. Then, it determines the relationship between roads lacking traffic information and the TLPs according to their related factors. After obtaining the TLPs, this method ascertains the weight of road links by calculating their similarities with TLPs based on the theory of fuzzy inference. Aiming at road links at different places, the dividing – conquering strategy and globe algorithm are also introduced to calculate the weight. These two strategies are used to address the excessively fragmented or lengthy links. The experimental results with the case of Newcastle show robustness in that the average Root Mean Square Error (RMSE) is 1.430 mph, and the bias is 0.2%; the overall RMSE is 11.067 mph, and the bias is 0.6%. This article is the first to combine the third law of geography with fuzzy inference, which significantly improves the estimation accuracy of road weights with incomplete information. Empirical application and validation show that the method can accurately predict vehicle speed under incomplete information.

Abstract It has become increasingly difficult for medical practitioners to recognize illness in recent years due to the emergence of new diseases from their myriad causes on a daily basis. Due in large part to inadequate diagnostic and monitoring infrastructure, a substantial amount of illness and death are associated with lung cancer (LC). The aim of the paper is to find lung cancer early and help patients receive curative treatment. Quitting smoking or never starting is the best way to mitigate the potential for disease-related death. As a result, cutting-edge detection and monitoring technologies must be developed to enable rapid, accurate, and timely diagnosis. Fuzzy logic (FL) is one of the best approaches to modeling complex and uncertain systems; therefore, it helps us deal with these challenges. Fuzzy expert system for lung cancer [FES-LC] detection and prediction on Internet of medical things (IoMT) is employed to overcome the challenges. Hence, an enhanced adaptive neuro-fuzzy inference framework [ANF-IF] is proposed in the current research. The cloud-based application of an adaptive neuro-fuzzy inference system yields four risk categories: not at risk, slightly at risk, moderately at risk, and severely at risk. New methods and theoretical frameworks have made it possible to diagnose LC in its earliest stages with the help of magnetic nanoparticles (MNPs), which allow researchers to overcome the limitations of conventionally slow diagnostic efficiency. The proposed system exhibits a precision of 93.4%, accuracy of 95.1%, specificity of 90.6%, sensitivity of 92.8%, false positive rate of 0.22%, false negative ratio of 0.18%, and classification accuracy of 98.2%. The proposed method outperforms all methods and provides better lung cancer detection accuracy than others.

Abstract Utilizing the direct current‐side electrical data resources of the photovoltaic power generation system on the FusionSolar platform, this study investigates the impact of hot spot faults on the output characteristics of photovoltaic strings and proposes a hot spot fault diagnosis method based on time series waveform characteristics. By analyzing the mechanisms of hot spot generation and evolution, as well as the characteristic differences in I–V curves and time series compared to other types of faults, the waveform variation patterns of hot spots in current and voltage time series are obtained. A function form suitable for hot spot fault waveform characteristics in time series graphs is constructed, and fault diagnosis feature vectors are extracted. Combining field operation and maintenance experience, a fuzzy reasoning fault diagnosis system is established to determine the causes and estimate the severity of hot spot faults. Experimental results indicate that hot spot faults have unique and corresponding variations in the current/voltage time series waveforms of the string output. The constructed function form can clearly represent the waveform variation patterns, and the established fuzzy reasoning system can achieve effective and reliable diagnosis of hot spot faults.

Abstract Recently, artificial intelligence (AI)-generated resources have gained popularity because of their high effectiveness and reliability in terms of output and capacity to be customized and broadened, especially in image generation. Traditional Chinese paintings (TCPs) are incomplete because their color contrast is insufficient, and object reality is minimal. However, combining AI painting (AIP) with TCP remains inadequate and uncertain because of image features such as patterns, styles, and color. Hence, an algorithm named variational fusion-based fuzzy accelerated painting (VF2AP) has been proposed to resolve this challenge. Initially, the collected TCP data source is applied for preprocessing to convert it into a grayscale image. Then, the feature extraction process is performed via fuzzy-based local binary pattern (FLBP) and brushstroke patterns to enhance the fusion of intelligent fuzzy logic to optimize the local patterns of textures in a noisy image. Second, the extracted features are used as inputs to the variational autoencoder (VAE), which is used to avoid latent space irregularities in the image and the reconstructed image by maintaining minimum reconstruction loss. Third, fuzzy inference rules are applied to avoid variation in the fusion process of the reconstructed and original images. Fourth, the feedback mechanism is designed with evaluation metrics such as area under the curve-receiver operating characteristic (AUC-ROC) analysis, mean square error (MSE), structural similarity index (SSIM), and Kullback‒Leibler (KL) divergence to enhance the viewer's understanding of fused painting images.

To ensure the safety of coal mine drilling operation and reduce losses caused by accidents, this study proposes a fuzzy-reasoning-based early prediction method for in-hole accidents during underground coal mine drilling processes. First, the mechanism of in-hole accidents during underground drilling in coal mines was analyzed, and the changes in different accident-related parameters were summarized. Second, based on the suddenness of different accidents, they were distinguished into sudden-change and slow-change accidents, and the corresponding features were extracted from short- and long-timescale information, respectively. Subsequently, a rule base was constructed based on the analysis of field data and manual experience, and different accident occurrence probabilities were determined using fuzzy reasoning to realize the early prediction of in-hole accidents in the coal mine drilling process. Finally, WinCC was used to design the upper computer interface for in-hole accident prediction in underground drilling processes in coal mines. It displays the results of three types of in-hole accident prediction: drill bit failure, stuck pipe, and bit drop. [ABSTRACT FROM AUTHOR]

In the face of increasingly complex and dynamic access scenarios, traditional access control exhibits limitations in flexibility, to some extent impairing the availability of resources. To address this issue, this paper proposed a context-aware adaptive access control model(CABAC). This model enhances flexibility and temporal efficiency by incorporating additional special authorization mechanisms to handle specific requests, ultimately improving resource availability. Utilizing a contextaware reasoning approach grounded in fuzzy logic to evaluate the contextual situation during user access, the system could make special authorization decisions, thereby achieving a flexible and fine-grained authorization capability suitable for dynamic environments. Introducing a trust mechanism confined users' special authorizations, preventing misuse of privileges. Simultaneously, tracking and monitoring user activities during sessions were conducted to provide adaptive access control capability. Experimental results demonstrate that CABAC can adapt to dynamic and complex access control scenarios, dynamically adjusting permissions according to different contexts, thereby enhancing resource availability. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Utilizing the direct current‐side electrical data resources of the photovoltaic power generation system on the FusionSolar platform, this study investigates the impact of hot spot faults on the output characteristics of photovoltaic strings and proposes a hot spot fault diagnosis method based on time series waveform characteristics. By analyzing the mechanisms of hot spot generation and evolution, as well as the characteristic differences in I–V curves and time series compared to other types of faults, the waveform variation patterns of hot spots in current and voltage time series are obtained. A function form suitable for hot spot fault waveform characteristics in time series graphs is constructed, and fault diagnosis feature vectors are extracted. Combining field operation and maintenance experience, a fuzzy reasoning fault diagnosis system is established to determine the causes and estimate the severity of hot spot faults. Experimental results indicate that hot spot faults have unique and corresponding variations in the current/voltage time series waveforms of the string output. The constructed function form can clearly represent the waveform variation patterns, and the established fuzzy reasoning system can achieve effective and reliable diagnosis of hot spot faults. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai University / Shanghai Daxue Xuebao is the property of Journal of Shanghai University (Natural Sciences) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An adaptive maneuvering target tracking algorithm based on fuzzy inference is proposed to deal with the low adaptive capacity of variable structure interacting multi-model algorithms for target maneuver uncertainty and measurement uncertainty in maneuvering target tracking. A two-stage maneuvering discrimination model based on fuzzy inference is designed, which uses the probability of models and residual weighted norm of the main model to infer the reliability of the main model and the possibility of maneuvering discrimination. The two-stage maneuvering discriminant is introduced into the framework of expected-model augmentation based on likely model-set (EMA-LMS). A kind of fuzzy inference-based EMA-LMS algorithm is proposed to adjust the parameter and strategy of model-set adaption online. This algorithm generates an expected model that is closer to the real motion model and makes better choices for model selection. The simulation results show that the proposed algorithm can strengthen the adaptive capacity for the uncertainty of target maneuver and measurement, and improve accuracy.

AbstractAccurately determining the spatiotemporal distribution of boiling heat flux in a single-bubble pool is crucial for understanding the pool’s heat transfer mechanism. However, direct measurement is challenging. Utilizing an inverse heat transfer approach, where part of the temperature measurement information of the boiling wall is employed, offers an effective solution to this issue. This study explores the application of the double decentralized fuzzy inference (DDFI) method to identify the distribution of single-bubble boiling heat flux in three-dimensional heat conduction systems. Through simulation experiments, we estimate the boiling heat flux distribution of single-bubble boiling and validate the DDFI method’s effectiveness through comparative analysis. Furthermore, the measured temperature data of the single-bubble boiling process is utilized to identify the heat flux distribution on the boiling surface using the DDFI method. We reconstruct the transient temperature field of the heating wall using inversion results, and verify the reliability of the reconstruction results. [ABSTRACT FROM AUTHOR]

This paper presents a novel fuzzy-augmented model reference adaptive voltage regulation strategy for the DC–DC buck converters to enhance their resilience against random input variations and load-step transients. The ubiquitous proportional-integral-derivative (PID) controller is employed as the baseline scheme, whose gains are tuned offline via a pre-calibrated linear-quadratic optimization scheme. However, owing to the inefficacy of the fixed-gain PID controller against parametric disturbances, it is retrofitted with a model reference adaptive controller that uses Lyapunov gain adaptation law for the online modification of PID gains. The adaptive controller is also augmented with an auxiliary fuzzy self-regulation system that acts as a superior regulator to dynamically update the adaptation rates of the Lyapunov gain adaptation law as a nonlinear function of the system's classical error and its normalized acceleration. The proposed fuzzy system utilizes the knowledge of the system's relative rate to execute better self-regulation of the adaptation rates, which in turn, flexibly steers the adaptability and response speed of the controller as the error conditions change. The propositions above are validated by performing tailored hardware experiments on a low-power DC–DC buck converter prototype. The experimental results validate the improved reference tracking and disturbance rejection ability of the proposed control law compared to the fixed PID controller. [ABSTRACT FROM AUTHOR]

This research explores "Ni–Al2O3" composite coatings produced through conventional electrodeposition, examining their corrosion resistance about variations in alumina concentration, bath temperature, and applied electrical current. Unlike previous studies, this research introduces a new fuzzy inference model, used in conjunction with polarization techniques, to analyze the impacts of the parameters. The model closely aligns with experiments, exhibiting 4.336% average error for corrosion rates. Such precision offers a promising pathway for optimizing corrosion resistance, an aspect that has not been sufficiently addressed in previous research. Findings highlight the crucial influence of Al2O3, showcasing minimal corrosion rates at 5 g/l at 30 °C and 15 g/l at 40 °C. Optimal corrosion resistance peaks at 15 g/l Al2O3, declining beyond this concentration. Elevated temperatures diminish resistance post 40 °C. Increasing Al2O3 concentration halves corrosion rates, demonstrating effective Al2O3 particle incorporation. Bath temperature and Al2O3 concentration synergy significantly reduce corrosion rates until 40 °C. However, beyond 40 °C, corrosion rates rise irrespective of current. The study underscores bath temperature's dominance over deposition current in influencing Ni–Al2O3 coating anti-corrosive properties. Employing fuzzy logic with only 60 experiments provides an economical approach for predicting corrosion rates, marking a substantial stride in developing high-corrosion-resistant metallic composites. [ABSTRACT FROM AUTHOR]

This investigation explores the effects of applying fuzzy time series (FTSs) based on neural network models for estimating a variety of spectral functions in integer time series models. The focus is particularly on the skew integer autoregressive of order one (NSINAR(1)) model. To support this estimation, a dataset consisting of NSINAR(1) realizations with a sample size of n = 1000 is created. These input values are then subjected to fuzzification via fuzzy logic. The prowess of artificial neural networks in pinpointing fuzzy relationships is harnessed to improve prediction accuracy by generating output values. The study meticulously analyzes the enhancement in smoothing of spectral function estimators for NSINAR(1) by utilizing both input and output values. The effectiveness of the output value estimates is evaluated by comparing them to input value estimates using a mean-squared error (MSE) analysis, which shows how much better the output value estimates perform. [ABSTRACT FROM AUTHOR]

Fuzzy inference has been applied widely in many fields, such as artificial intelligence, decision-making, classification, and so on. Generalized hypothetical syllogism (GHS), as a generalization of hypothetical syllogism (HS) in classical logic, can measure the validity of compositional rule of inference (CRI) method. To promote the capability of CRI method, this paper mainly studies the GHS property of well-known fuzzy implications with a t-norm and constructs a CRI method based on the GHS property. First, some t-norms are found for some R-implications, (S, N)-implications and QL-implications, such that they satisfy the GHS property, respectively. And then, it is revealed that continuous (S, N)-implications generated by strict t-conorms, f-implications with f (0) = ∞ , and g-implications with g (1) = ∞ do not satisfy the GHS property with any t-norm. Finally, we propose a CRI method using the fuzzy implications which satisfy the GHS property. Two examples are also provided to illustrate our proposed method. [ABSTRACT FROM AUTHOR]

• Reliable control method for multi-split backplane cooling system in data centers is proposed. • A variable condensing pressure method is proposed based on fuzzy inference. • Condensing pressure set-point can be obtained based on EEV-based fuzzy inference. • COP can be increased by 13.3% compared with the conventional control method. In contrast to existing room-level cooling systems, rack-level cooling systems can solve local hot spot issues due to their on-demand cooling way with higher energy efficiency and higher supply cooling temperatures. However, the multi-split backplane cooling system, which is a typical rack-level cooling way, makes it easy to cause insufficient refrigerant supply problems due to higher evaporating pressure under the same ambient condition. To solve above problems, a suitable condensing pressure relative to evaporating pressure is a critical optimized parameter, which needs to follow with the change of ambient condition. Therefore, a variable pressure differential control method between the condensing side and the evaporating side is proposed based on online fuzzy inference. A Modelica-based simulation model is built to validate the proposed method. Results show that the proposed method can ensure the terminal cooling effect at different ambient temperatures. Compared with the fixed condensing pressure control method, the proposed method shows an increase of 13.3% in system coefficient of performance (COP) with lower condensing pressure. [ABSTRACT FROM AUTHOR]

The interest of the crowd plays a vital role at national level elections not only to predict the influence of participating parties but also to make marketing strategies for the campaign. Beyond person-to-person interaction, social media is also used by society to put their views, comments, and interest toward political parties and their candidates. But, due to the large volume, variety, and velocity of social media data, it becomes difficult to analyze. Moreover, the extracted information from social tweets also raises issues of biased information. Here, in this work, a fuzzy inference supportive framework is proposed to study crowds' preference on social media platforms to make campaign strategies by the political parties in national-level elections. The proposed approach utilizes tweets from Twitter, LinkedIn, and Instagram to predict the crowd's influence on political parties. Further, the approach can be utilized by the parties to change the campaign strategy. In experimentation, the approach is tested on the real-time dataset collected from the social network websites, and found that the proposed approach is performing well to predict the user's interest. Also, when compared with existing methods, the proposed approach's performance is found significant over other methods. [ABSTRACT FROM AUTHOR]

With the advancement of industrial manufacturing, the reliability and safety of mechanical equipment have increasingly become a focus of attention. Overheating failures, as one of the common types of malfunctions, have emerged as crucial areas of research for enhancing production efficiency and ensuring the stable operation of equipment. However, traditional fault diagnosis methods exhibit significant shortcomings in terms of real-time performance and accuracy, especially when analyzing thermal stress under complex conditions. To address this issue, this study proposes a method for diagnosing and preventing overheating failures in mechanical equipment based on numerical analysis of temperature and thermal stress fields. Initially, a fluid-thermal-structural coupled model incorporating transient temperature fields was established, overcoming computational instabilities caused by overheating deformations and large deformation meshes through improved numerical calculation methods. Subsequently, by integrating fuzzy inference techniques, the model's ability to judge uncertain information was enhanced, thereby improving the accuracy and reliability of fault analysis. This research not only optimizes the fault diagnosis process but also provides a new theoretical basis and technical approach for the prevention of mechanical equipment failures, offering significant engineering application value. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Power distribution utilities effort to ensure the quality of energy to their consumers and the reliability of their power distribution system. It is necessary that maintenance activities are planned with the aim of maintaining or improving reliability indicators in supply to consumers. In this paper, a computational model based on integer nonlinear multiobjective programming is presented to improve the maintenance of equipment in the power distribution system. Since it is a reliability-centered approach, a probabilistic failure model is first used to obtain equipment reliability values at each time point through fuzzy inference. Three objective functions are optimized: i) minimizing maintenance cost, ii) minimizing failure frequency, and iii) maximizing equipment reliability. The optimization problem is also formed by three sets of constraints: i) individual and collective continuity indicators; ii) task execution time; and iii) maintenance limit for each type of equipment. Lichtenberg's algorithm is used to solve the model. A case study is performed for a feeder section consisting of twenty-eight distribution equipment. The results obtained using the Pareto constraints show scenarios that can help maintenance teams to make decisions and develop the preventive maintenance planning. Adding constraints on the duration and frequency of collective interruptions indicators improves the power quality of the distribution system; however, it requires an increase in investment by 36%.

This paper presents a new watermarking method to improve the robustness and transparency of extracted and host images. The embedding process is based on decomposing of pyramidal directional filter bank and triangular matrix, while the watermark extraction process is based on Mamdani fuzzy logic. In this design, in order to obtain efficient robustness and transparency, the Harris hawks optimization algorithm is used to find the best value of embedding factor. For this purpose, in the embedding algorithm, pyramid directional filter bank decomposition is utilized and accordingly the approximation sub-bands are divided into 8*8 non-overlapping blocks. Moreover, by decomposing the triangular matrix, which embeds the watermark bits in the matrix element, the use of Mamdani implication and the product inference engine have led to an efficient watermark extraction. The simulation results show that the quality of the watermarked image is equal to 60.6dB. Furethermore, applying the proposed algorithm is strong against attacks.

A flight safety warning method based on reachability analysis and fuzzy inference is proposed against aircraft icing. A nonlinear model of iced aircraft with uncertainty is proposed based on the existing research results on the effects of icing and the uncertainty in icing detection. To deal with the uncertainty caused by icing, reachability analysis is used to estimate the safe flight envelope of iced aircraft. On this basis, fuzzy inference is employed for flight safety warning which can be used to enhance the pilot's situational awareness in icing encounters. Simulations of the GTM (Generic Transport Model) aircraft show that, the proposed method has the potential to further increase the flight crew awareness about the risk of losing control in flight under icing. [ABSTRACT FROM AUTHOR]

Parkinson's Disease (PD) is a brain‐related disease that eventually causes disability and disrupts a person's normal life. Most physicians and researchers try to diagnose it quickly and treat it on time. In recent years, computer science and the field of artificial intelligence (machine learning) have helped researchers find a way to detect the disease early. This article proposes a method that diagnoses Parkinson's disease by analyzing the hand drawing shaped by the individuals using Bi‐Directional Long Short‐Term Memory (Bi‐LSTM) neural network. In addition, this paper proposes a Fuzzy Inferential Classifier for the Dense layer, which classifies the output of LSTM Blocks to the associated classes by modifying the Soft‐max function. Our decision to propose this classifier was because, in some cases, hand‐drawing data related to people with Parkinson's disease have no significant difference with the healthy subjects for the distinguishing and are often very similar to each other. A standard dataset has been used in this paper, which includes spirals drawn test (including static spiral, dynamic spiral, and stability specific point) by a group of healthy people and people with Parkinson's disease. The proposed method has reached 97%, 98.5%, and 100% accuracy rates for the three mentioned spiral tests with a smoother training loss and accuracy plots. In addition, the results outperform state‐of‐the‐art research conducted on this dataset and show at least more than 2.5% improvement in the accuracy rate in comparison. [ABSTRACT FROM AUTHOR]

In the context of the burgeoning cloud-edge collaboration paradigm, powered by advancements in the Internet of Things (IoT), cloud computing, and 5G technology, this paper proposes a task orchestrating strategy for cloud-edge collaborative environments based on intuitionistic fuzzy sets. The proposed strategy prioritizes efficient resource utilization, minimizes task failures, and reduces service time. First, WAN bandwidth, edge server virtual machine utilization, delay sensitivity of the task, and task length are used to determine whether the task should be executed on the cloud or edge device. Then, the cloud-edge collaborative decision-making algorithm is used to select the task's target edge servers (either the local edge servers or the neighboring edge servers). Finally, simulation experiments are conducted to demonstrate the effectiveness and efficacy of the proposed algorithm. [ABSTRACT FROM AUTHOR]