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Proper equipment maintenance can significantly reduce overall operating costs by boosting productivity. Management personnel often consider maintenance an expense; however, a more positive approach is to view maintenance as a profit center. Considering this new perspective, the requirements for maintenance management have changed drastically from the traditional concept of fix-it-when-broken to a more complex approach that entails adopting a maintenance strategy for a more integrated approach and alignment. This paper presents a Goal Programming (GP) approach to define the most cost-effective method for maintaining some critical pumps in the paper industry. The failure mode and effects analysis (FMEA) criteria consider each machine failure's occurrence, severity, and detection. After determining the weights, the optimal strategy for each failure is calculated by solving the goal programming. Thus, FMEA can be used to design a network structure for each pump failure mode. For this purpose, the limitations and goals affecting the model are obtained from research literature and interviews with experts. Regarding resource utilization and failure reduction, the results demonstrate that predictive and preventive maintenance strategies are superior to corrective strategies. Therefore, these strategies can predict failures, provide helpful information to maintenance managers, and limit the negative aspects of a failure regarding safety and cost. [ABSTRACT FROM AUTHOR]

Recycling of industrial wastewater meeting quality standards for agricultural and industrial demands is a viable option. In this study, paper and pulp industrial wastewater were treated with three biological treatments viz. aerobic, anaerobic and sequential (i.e. 20 days of anaerobic followed by 20 days of aerobic cycle), associated with simulation modeling by Mamdani Fuzzy Logic (MFL) model of some selected parameters. Electric air diffuser and minimal salt medium in sealed plastic bottles at control temperature were used for aerobic and anaerobic treatments, respectively. The significant reduction in chemical (COD: 81%) and biological oxygen demand (BOD: 71%), total suspended (TSS: 65%), dissolved solids (TDS: 60%) and turbidity (68%) was recorded during sequential treatment. The treated water was irrigated to determine its phytotoxic effects on seed germination, vigor and seedling growth of mustard (Brassica campestris). Sequential treatment greatly reduced phytotoxicity of wastewater and showed the highest germination percentage (90%) compared to aerobic (60%), anaerobic (70%) treatments and untreated wastewater (30%). Regression analysis also endorsed these findings (R2 = 0.76–0.95 between seed germination, seedling growth and vigor). MFL technique was adopted to simulate sequential treatment process. The results support higher performance of MFL model to predict TDS, TSS, COD, and BOD based on the physico-chemical water quality parameters of raw wastewater, time of treatment and treatment type variation. Based on these findings, we conclude that the sequential treatment could be a more effective strategy for treatment of pulp and paper industrial wastewater with efficiency to be used for agricultural industry without toxic effects. Image 10 • Pulp and paper wastewater was subjected to aerobic, anaerobic and sequential treatment. • Sequential treatment significantly reduced COD, BOD, TSS, TDS and turbidity. • Mamdani Fuzzy Logic technique was adopted to simulate sequential treatment process. • Sequentially treated wastewater improved seed germination, seedling growth and vigor index. • Sequential treatment is more efficient as compared to sole aerobic and anaerobic treatment. [ABSTRACT FROM AUTHOR]

In recent years, the pulp and paper industry has changed a lot and products have been constructed in quite different methods using new technologies. It can be due to the emergence of challenges and constraints such as shortage of raw materials, energy consumption (fossil fuels, electric, etc.), management of the resulting pollutants, environmental issues, ongoing legal requirements and cleaner production strategies. In Iran, the pulp and paper industry's technological development is not keeping pace with global growth. Iran's pulp and paper mills supply a small fraction of domestic market. The reasons can be due to old technologies being used in factories, aging machinery, low productivity of production systems and lack of coordination with modern technologies. It leads to low-quality of paper products and willingness to buy local products. The aim of this study is to select the most appropriate technologies in the pulp section, due to high impact of pulp quality on the quality of final paper. Using a 2-filterd fuzzy decision-making, hierarchical structure of research is designed to assess technological alternatives according to the selected criteria. Findings show that after passing through two filters, Kraft method was chosen as the most appropriate method. The Kraft process has still many advantages like the high quality of the produced pulp and self-sufficiency in energy supply. This framework offers an appropriate procedure for identifying and selecting technologies that has not been used in past researches. Using this framework facilitates using a clear procedure for evaluating and selecting technology and prevents future losses for organizations. [ABSTRACT FROM AUTHOR]

In the very few recently published paper by Alavidoost et al. [1] , they proposed a novel fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problem, in continue of their previous presented work by Alavidoost et al. [2] , as a modification on genetic algorithm for assembly line balancing with fuzzy processing times. Despite the fact that both of them are well-written, and completely discussed their contributions, this note looks forward to collate these papers together. Likewise provides the correct order of the figures in [1] matching with their corresponding caption. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of this paper is to present a novel technique for analyzing the behavior of an industrial system stochastically by utilizing vague, imprecise, and uncertain data. In the present study two important tools namely Lambda-Tau methodology and particle swarm optimization are combinedly used to present a novel technique named as particle swarm optimization based Lambda-Tau (PSOBLT) for analyzing the behavior of a complex repairable system stochastically up to a desired degree of accuracy. Expressions of reliability indices like failure rate, repair time, mean time between failures (MTBF), expected number of failures (ENOF), reliability and availability for the system are obtained by using Lambda-Tau methodology and particle swarm optimization is used to construct their membership function. The interaction among the working units of the system is modeled with the help of Petri nets. The feeding unit of a paper mill situated in a northern part of India, producing approximately 200ton of paper per day, has been considered to demonstrate the proposed approach. Sensitivity analysis of system''s behavior has also been done. The behavior analysis results computed by PSOBLT technique have a reduced region of prediction in comparison of existing technique region, i.e. uncertainties involved in the analysis are reduced. Thus, it may be a more useful analysis tool to assess the current system conditions and involved uncertainties. [Copyright &y& Elsevier]

The communication security of unmanned aerial vehicles (UAVs) or drones based on flying ad hoc networks (FANETs) can be enhanced and maintained by assessing vulnerabilities, threats, and attacks. Thus, trust, high mobility, and complex communication interfaces are crucial for effective coordination and segregation of malicious drones from the genuine. A new efficient honesty-based detection scheme for malicious drones has been proposed to distinguish between intentional and unintentional misbehavior of the UAVs. The proposed scheme configures a drone system for packet transmission within the FANET, specifying buffer size and packet size variables to control data flow and prevent congestion. It then computes energy used for packet reception and transmission, optimizes energy consumption, and evaluates the drone's mobility through the link stability index (LSI) and honesty UAV. The honesty UAV parameter categorizes UAVs into unintentional malicious and intentional malicious categories. Fuzzy logic helps identify intentional and unintentional misbehaving drones and improves performance in the network. It continuously generates the malicious UAV detection mechanism by assessing the parameters i.e., energy, pattern of movements, and the honesty score about the UAVs as a whole. Thereby a dynamic rating system to adeptly identify and differentiate the cooperative and non-cooperative nature of drones from the network is assimilated in the paper. Simulation results show the proposed scheme approximately aligns with the actual cases for varying numbers of malicious drones. Finally, the observation of the simulation result reflects substantial enhancement in performance metrics, with a significant cutback in end to end delay and packet delivery ratio by 10–30% and 20–50% respectively, contrasting with the existing techniques. [ABSTRACT FROM AUTHOR]

On-Orbit-Servicing missions aim to improve the operative life of orbiting satellites. Among the studied technologies, the most promising are satellites equipped with one or more robotic arms (space robots). One robotic arm might be employed to establish a rigid connection with the target satellite, while the other performs the servicing operations. Nowadays, the control of the capture tool is managed by the computer of the space robot. However, it is possible to distribute the control tasks between the capture tool and the space robot. For this purpose, this paper presents the development of the SMArt Capture Kit (SMACK). SMACK is a standalone capture system equipped with sensors, actuators, and an integrated computer. In this fashion, the capture tool executes the guidance and navigation algorithms, and the control of the actuators; so that the space robot acts as the "positioning" system for the capture tool. The paper provides a description of all the subsystems of SMACK and their experimental validation. The tests of SMACK at the system level, confirm its capabilities to autonomously perform the capture the target interface. • Smart capture tool. • Autonomous tool with integrated sensors, actuators and computer. • Distributed control. • Use of fuzzy logic to develop the model of a custom sensor. • Use of fuzzy logic to create the approach trajectory. [ABSTRACT FROM AUTHOR]

Wind turbines (WTs) have highly nonlinear and uncertain dynamics due to aerodynamic complexity, mechanical factors, and fluctuations in wind conditions. Turbulence and wind shear add complexity to modelling, especially in constant power region (region 3). Thus, an effective control design demands a deep understanding of the nonlinearities and uncertainties. This paper suggests a novel model-free reinforcement learning (RL) collective pitch angle controller to operate efficiently in region 3. The proposed controller stabilizes generator speed, maximizes power output, and minimizes fluctuations while accommodating system uncertainties, nonlinearity, and pitch limits. The disparity between WT dynamics due to wind speed perturbations and uncertainties is measured using a gap-metric criterion. The controller design adopts a deep deterministic policy gradient (DDPG) algorithm to train six agents in a medium-fidelity WT environment at different mean wind speeds to ensure the controller's robustness. Initially, imitation learning is used for efficient sample collection to fasten training convergence. Afterwards, the agent learns by interacting with the environment. After the training, the pitch control outputs from multi-trained agents are processed by a fuzzy system to have smooth transitions under different operating conditions. The resulting fuzzy DDPG (F-DDPG) controller is deployed to obtain the optimal pitch control action. The performance of the proposed F-DDPG controller is compared to the gain-scheduled PI (GSPI), Linear-Quadratic-Regulator (LQR), and single-DDPG-agent controllers. The controllers are simulated in high-fidelity onshore and offshore 5-MW WT environments using the OpenFAST/MATLAB simulation tools. The results reveal the superiority of the proposed controller in generalizing its optimal performance in different operating conditions. • Proposing novel collective pitch control system using deep reinforcement learning for wind turbines (WTs) to operate efficiently in constant power region (Region 3). • Building a modified version of the DDPG algorithm to fastening the training of multiple agents at various wind speed operating points. • Deployement of a fuzzy logic system to smooth the transition between the pitch control signals from the trained agents to overcome the stochastic time-varying dynamics of the wind turbine. • Enhancing the performance of the high-fidelity NREL Open-FAST WT model in technical and economic terms compared to traditional model-based controllers and single DDPG model-free controller. • Achieving stabilized generator speed, minimum fluctuations, robustness of operation, and maximized power output at all operating points of Region 3. [ABSTRACT FROM AUTHOR]

This paper focuses on online recorded-data-based composite adaptive fuzzy bipartite consensus control for uncertain fractional-order multiagent systems with interconnected terms and external disturbances by employing a switched-threshold-based event-triggered mechanism (ETM) under the backstepping structure. Fuzzy logic system is used as a universal function approximation to deal with function uncertainties that are not prone to model in the system. A new composite learning adaptive parameter design scheme that synthesizes both prediction error and tracking error is developed to enhance the tracking performance, where the prediction error is raised from the utilization of online recorded data and instantaneous data. A unique switched-threshold-based ETM is introduced, in which the information transmission between the sensor and the controller is imposed on one of the individuals. One merit of this work consists in that it can automatically and rapidly switch and adjust between the fixed threshold and relative threshold ETM according to the amplitude of input signals to balance the network resources and impede the occurrence of pulse phenomenon. In addition, it is theoretically proven that the proposed scheme can ensure that all internal signals of the closed-loop system are bounded and achieve local bipartite consistent errors through the fractional Lyapunov stability criterion. Finally, a numerical example is provided to confirm the feasibility of the proposed approach. • A bipartite consensus control scheme for fractional-order multi-agent system is proposed. • An online data recording algorithm for weight update parameters is proposed to further enhance the system performance. • The threshold for switched event-triggered mechanism can be arbitrarily selected to abate the trigger frequency. [ABSTRACT FROM AUTHOR]

In this paper the risks that affect highway construction projects duration are studied by qualitative and quantitative approaches. For the qualitative analysis the consistency of expert opinions was evaluated, and two methods were applied to identify critical risks that affect local projects. The quantitative analysis studied the impact of those risks over the activities of a local project and also the relationship between risks using fuzzy logic and Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

Due to the cultural and traditional diversity of the Indian environment, the successful implementation of self-automated and smart architectures is very challenging. As the IoT expands the network of devices, it also increases the hazards with increasing demands. This paper aims to propose an efficient model with the combination of interdisciplinary aspects of Indian building automation and numerous factors of Indian cultural differences. The proposed fuzzy-colored Petri-nets-based ubiquitous framework utilizes the capabilities of ubiquity for the diversified culture of Indian Building Automation systems. The proposed algorithms establish the Wi-Fi component connectivity and the working of the LED pin. Further, the modules of the Wi-Fi component connectivity, set up function and get values function to illustrate the working principle of the proposed model. Lastly, the integrated system using the FCPN tools. The significance of developing the proposed model is in its usefulness, usage of maximum ideas of different modeling techniques, cultural support, and its conversion into a formal qualitative and quantitative model. [ABSTRACT FROM AUTHOR]

Computer games are widespread nowadays and enjoyed by people of all ages. But when it comes to kids, playing these games can be more than just fun—it's a way for them to develop important skills and build emotional intelligence. Facial expressions and sounds that kids produce during gameplay reflect their feelings, thoughts, and moods. In this paper, we propose a novel framework that integrates a fuzzy approach for the recognition of emotions through the analysis of audio and video data. Our focus lies within the specific context of computer games tailored for children, aiming to enhance their overall user experience. We use the FER dataset to detect facial emotions in video frames recorded from the screen during the game. For the audio emotion recognition of sounds a kid produces during the game, we use CREMA-D, TESS, RAVDESS, and Savee datasets. Next, a fuzzy inference system is used for the fusion of results. Besides this, our system can detect emotion stability and emotion diversity during gameplay, which, together with prevailing emotion report, can serve as valuable information for parents worrying about the effect of certain games on their kids. The proposed approach has shown promising results in the preliminary experiments we conducted, involving 3 different video games, namely fighting, racing, and logic games, and providing emotion-tracking results for kids in each game. Our study can contribute to the advancement of child-oriented game development, which is not only engaging but also accounts for children's cognitive and emotional states. [ABSTRACT FROM AUTHOR]

A new approach for Fuzzification and Defuzzification processes of a high degree of overlapping between the linguistic variables through proportional and relative-dynamic-scale membership functions is presented in this paper. Most of related works that utilized type-1 and 2 fuzzy logic algorithms are confused and questionable from two points of view: (1) the overlapping between linguistic variables values starts almost from the midst of each other; so, what will be happened if it starts before the midst i.e., increasing the degree of overlapping, - (2) the linguistic variables are commonly represented by symmetrical membership functions form. It has been found that the current type 1 and 2 fuzzy logic are no longer suitable to deal with highly inference between linguistic variables. Thus, a novel algorithm, called laser simulator logic, is introduced in this paper to deal with the issue of highly inference of linguistic variables membership functions, which depends mainly on a proportional and relative-dynamic scale of membership function that will change the scale of membership continuously based on the crisp input/output. Results show that the laser simulator performs better than both type-1 and 2 fuzzy logic when there is a high degree of overlapping between the linguistic variables. [ABSTRACT FROM AUTHOR]

This paper considers tracking control of non-affine pure-feedback systems with uncertain disturbances; a new adaptive fuzzy command filtered backstepping controller is presented. First, the non-affine difficulty of pure-feedback systems is solved with the help of the mean-value theorem, and the fuzzy logic systems (FLSs) are adopted to estimate the system uncertainties which contain external disturbances. Next, a novel command-filtered technology is proposed to avoid the so-called "explosion of complexity" in the backstepping design process. The compensation mechanism is employed to eliminate the shortcoming of the traditional dynamic surface control. The presented control scheme ensures that all closed-loop signals are bounded, and the system output can track the given reference signal. The main contribution of this paper is that the developed approach is not only generalized to uncertain non-affine pure-feedback systems, which extends the practical applications of classical command filtered schemes, but also has an error compensation system, which improves tracking performance in comparison with existing dynamic surface control proposals. Finally, several simulation experiments validate the effectiveness and superiority of the proposed control scheme. • A fuzzy adaptive command filtered controller is first generalized to pure-feedback systems with uncertain external disturbances. • The running efficiency simulation shows that the proposed control algorithm reduces computational complexity by about 59% compared to the DSC algorithm. [ABSTRACT FROM AUTHOR]

This paper introduces an integrated path tracking control strategy for autonomous vehicles. The proposed control strategy is based on a multi-input multi-output linear model predictive control (LMPC) with a fuzzy logic switching system. The designed MPC is based on Laguerre networks. The main target of the designed MPC is to produce the optimal control signals of the steering angle and the angular velocity while considering the physical constraints of the control signals and the measurements noise. Since the vehicle model is highly nonlinear and is operated over a wide range of operating points, different linearized models are obtained. The controller parameters for each linear model are designed and tuned. The gab metric analysis is used to select a number of these models to simplify the design of the proposed controller. Then, these models are combined using a fuzzy logic controller to switch between them. To test the proposed controller performance, different paths are generated using path planning algorithms. These paths simulate different vehicle maneuvers scenarios. The simulation results show that the designed tracking controller has a tracking performance on different designed paths better than that of a Linear quadratic gaussian (LQG) tracking controller, discussed in this paper. • MIMO model predictive control is designed based on Laguerre filter for autonomous vehicles. • Fuzzy system is used to smoothly switch between linearized models. • Gap-metric analysis is used to reduce the number of the linearized models. • Proposed multi-model controller perfectly tracks paths regardless their complexity. [ABSTRACT FROM AUTHOR]

Semi-supervised clustering aims to guide the clustering by utilizing auxiliary information about the class labels. Among the semi-supervised clustering categories, the constraint-based approach uses the available pairwise constraints in some steps of the clustering procedure, usually by adding new terms to the objective function. Considering this category, Semi-supervised FCM (SSFCM) is a semi-supervised version of the fuzzy c -means algorithm, which takes advantage of fuzzy logic and auxiliary class distribution knowledge. Despite the performance enhancement caused by incorporating this extra knowledge in the clustering process, semi-supervised fuzzy approaches still suffer from some problems. All the data attributes in the feature space are assumed to have equal importance in the cluster formation, while some features may be more informative than others. Thus the feature importance issue is not addressed in the semi-supervised category. This paper proposes a novel Semi-Supervised Fuzzy c -means approach, which is designed based on Feature-Weight, and Cluster-Weight learning, named SSFCM-FWCW. Inspired by the SSFCM, a fuzzy objective function is presented, which is composed of (1) a semi-supervised term representing the external class knowledge; (2) a feature weighting; and (3) a cluster weighting. Both feature weights and cluster weights are determined adaptively during the clustering. Considering these two techniques leads to insensitivity to the initial center selection, insensitivity to noise, and consequently helps to form an optimal clustering structure. Experimental comparisons are carried out on several benchmark datasets to evaluate the proposed approach's performance, and promising results are achieved. The Matlab implementation source code of the proposed method is publicly accessible at https://github.com/Amin-Golzari-Oskouei/SSFCM-FWCW. • This paper proposes a novel Semi-Supervised Fuzzy C-means approach based on Feature-Weight and Cluster-Weight learning. • An adaptive weight for each feature in each cluster applies based on its importance in forming the clusters. • The weight of the clusters is calculated dynamically to decrease cluster center initialization sensitivity. • Considering the conjunction of feature and cluster weighting helps form an optimal clustering structure. [ABSTRACT FROM AUTHOR]

This paper introduces a novel methodology to detect and identify faults for a class of autonomous nonlinear systems. In the proposed design, a fuzzy extended system observer (FESO) based on the Mandami-type fuzzy system is used to estimate the fault that is considered to be the extended system state. In this method, the Mamdani-type fuzzy system is based on a single-input single-output (SISO) where the observer error is considered as the fuzzy input variable. Additionally, the stability analysis under Lyapunov criteria verifies that the solutions of proposed FESO are ultimately bounded. Finally, simulation examples are given to corroborate the feasibility of the proposed FESO. • This paper presents a novel fuzzy extended state observer in order to solve the fault detection and identification problem. • The proposed observer is based on a single-input single-outpu Mamdani-type fuzzy inference system. • The Lyapunov study assures that the solutions of the proposed FESO are ultimately bounded. • The tuning of the fuzzy system, allows to achieve a family of fuzzy gains and improves the rate of convergence. • Numerical simulations confirm the theoretical results. [ABSTRACT FROM AUTHOR]

• This paper presents a novel application of AHA to tune the controllers parameters. • The super twisting sliding mode controller is applied to microgrids. • The results of proposed model are compared with other recent models. • The proposed model is verified by comparing its results with the measured results. It is essential to improve optimal control for an autonomous microgrid operation. This paper presents a novel application of an artificial hummingbird algorithm for achieving optimal control of an autonomous microgrid. It also suggests a comparative analysis for the optimal super-twisting sliding mode control with both optimal fuzzy logic control and optimal proportional-integral control. The proposed super-twisting sliding mode controller parameters are adequately designed by using the AHA optimization method; meanwhile, the FLC and PI controllers are designed by using the African vultures and the Gorilla troops optimization algorithms, respectively. A MATLAB/Simulink environment is used to verify the effectiveness of the suggested microgrid control methods. The overall system detailed approaches for modeling, designing, and controlling are introduced. The investigation of the ST-SMC approach is also tested in a real-time setting. OPAL-RT 4510 rapid control prototyping and OPAL-RT 8660 data acquisition are used to implement the suggested controller. The verification results prove the superiority of the ST-SMC based on the AHA optimization technique compared with other optimal control techniques. [ABSTRACT FROM AUTHOR]

Sensitive information can have its security compromised by unintentional electromagnetic emissions from the information technology equipment (ITE) being used to process it. It is important to assess the likelihood of a potential compromise, and this requires radio frequency (RF) engineering expertise to predict the likelihood of the vulnerability occurring. This paper describes the development of a fuzzy inference system that can be used to assess the radiated and conducted vulnerability likelihood of unintentional electromagnetic emanations. The system has the potential to be a valuable tool for cybersecurity practitioners without RF expertise. The system has been tested on office-based ITE devices, and it is effective in predicting the likelihood of radiated and conducted vulnerabilities occurring. Areas of future work include extending the fuzzy inference system to use RF propagation models and enabling it to make vulnerability likelihood predictions after countermeasures have been applied. [ABSTRACT FROM AUTHOR]

Tourist trip design is a fast-growing area of research. Tourist interest, budget, travel style, safety, and the existence of travelers with special needs (for example, wheelchair accessibility) are some of the elements to consider for obtaining personalized routes. Including these elements in a single mathematical model can considerably complicate the solution process. Also, route decision-making is affected by the context (health, social, political, economic, etc.) in which decisions are made. In this paper, the first aim is to propose a three-step methodology to obtain contextualized solutions to a tourist trip design problem (TTDP) with time-dependent recommendation factors. The methodology consists of (1) providing a basic TTDP model that avoids the complexity of including contextual information, (2) obtaining a set of solutions to the problem using a Modeling to Generate Alternatives (MGA) approach, and (3) using a recently developed a posteriori method to include the contextual information through fuzzy propositions. The second aim of the paper is to evaluate three algorithmic strategies for the MGA step. Lastly, considering a context for people with mobility impairments, an example is solved using real data. The results show the usefulness of the proposed methodology in solving the TTDP with contextual information. • A tourist trip design problem with time-dependent recommendations factors is solved. • A methodology to obtain contextualized solutions is developed. • An accessibility context is modeled with fuzzy propositions. • Solutions are obtained by using the modeling-to-generate-alternatives approach. • Best solutions are suitable for the accessibility context. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has become an essential part of our daily lives. However, with the increasing use of IoT, the number of botnet attacks targeting resource-constrained IoT devices is also on the rise. To mitigate these threats, intrusion detection systems (IDSs) have been developed. However, traditional IDSs based on heavyweight deep/machine learning, fuzzy logic, rough set theories, or data mining techniques, often lack in detection accuracy and energy efficiency. Therefore, there is a crucial need for more lightweight, accurate, and energy-efficient IDSs capable of detecting a broad spectrum of cyber attacks. This paper presents a solution to these challenges by introducing lightweight and accurate IDSs that use a stochastic gradient descent classifier (SGDC) and four feature-selection algorithms based on a ridge regressor. The hyperparameters of the SGDC algorithm and ridge regressor model were fine-tuned to enhance the accuracy of IDSs while reducing computational complexity. Moreover, the fine-tuned feature selectors were used to decrease the dataset's dimensionality and improve the accuracy of IDSs. To evaluate the proposed IDSs, three network traffic datasets (KDD-CUP-1999, BotIoT-2018, and N-BaIoT-2021) were used. The systems achieved an average accuracy of 92.69%, and the number of features was reduced by an average of 79.93%. The results demonstrate that the proposed systems can be utilized for lightweight IDSs on resource-constrained IoT devices. Overall, this paper presents a significant contribution to the field of IDSs for IoT devices by offering an efficient and accurate solution. The proposed lightweight IDSs have the potential to enhance IoT security and privacy, safeguarding sensitive IoT data. • SGDC-based lightweight IDSs were developed for resource-constrained IoT devices. • The optimal hyperparameters of SGDC were determined with a grid-search algorithm. • The fine-tuned SGDC boosted accuracy and reduced IDSs packet processing time greatly. • The ridge regression-based feature selectors determined the most efficient features. • The fine-tuned feature selectors reduced feature space greatly and improved accuracy. [ABSTRACT FROM AUTHOR]

Multi-drone cooperative transport has been extensively studied in literature. However, few issues are still required to be addressed to make such systems technologically sound. One such issue is that of control of such systems when the center of gravity of the object being transported is unknown and/or varies with time. Few examples in which the center of gravity is either offset (does not coincide with the geometric center of the object) or moves continuously with time include package delivery, pesticide spraying, and aerial car. This paper presents a decentralized adaptive control approach for a multi-drone cooperative transport system where the center of gravity of the system is offset and/or moving. This paper proposes a fuzzy logic based force-torque feedback controller along with two novel adaptive control laws to estimate the payload's center of gravity and/or its velocity. Simulation results validate the effectiveness of the proposed control technique as compared other methods in literature. [ABSTRACT FROM AUTHOR]

The main objectives of this study were to 1) review the literature on the applications of soft computing concepts to the field of human factors and ergonomics (HFE) between 2013 and 2022 and 2) highlight future developments and trends. Multiple soft computing methods and techniques have been investigated for their ability to address various applications in HFE effectively. These techniques include fuzzy logic, artificial neural networks, genetic algorithms, and their combinations. Applications of these methods in HFE have been highlighted in one hundred and four articles selected from 406 papers. The results of this study help address the challenges of complexity, vagueness, and imprecision in human factors and ergonomics research through the application of soft computing methodologies. • We reviewed the literature on the applications of soft computing concepts to the field of HFE. • Multiple soft computing methods have been investigated for their ability to address various applications in HFE effectively. • One hundred and four articles selected from 406 papers. • The results of this study can help address the challenges of complexity, vagueness, and imprecision in HFE. [ABSTRACT FROM AUTHOR]

In this paper, a combination of the interval type-2 fuzzy logic controller (IT2FLC) with the fractional-order proportional–integral–derivative (FOPID) controller, namely optimal interval type-2 fractional-order fuzzy proportional–integral–derivative controller (OIT2FOFPIDC), is developed for enhancing the seismic performance and robustness in seismic structural control applications. Based on the energy concepts, a decentralized framework of the OIT2FOFPIDC is proposed for easy and simple implementation in structures during earthquakes. For this purpose, a coot optimization algorithm (COA), as a powerful optimization algorithm, is also applied to adjust the membership functions (MFs), scaling factors, and the main controller parameters. Three controllers, namely optimal type-1 fuzzy proportional–integral–derivative controller (OT1FPIDC), optimal interval type-2 fuzzy proportional–integral–derivative controller (OIT2FPIDC), and optimal proportional–integral–derivative controller (OPIDC), are also proposed for comparison purposes. The seismic performances of the suggested controllers are examined with the evaluation of nine seismic performance indices and different ground accelerations in a 6-story smart structure equipped with two dampers. The robustness of the four controllers in the presence of the stiffness uncertainties is also compared in this study. On average, a reduction of 25.0%, 18.8%, and 18.5% in peak displacement, inter-story drift, and acceleration of stories is obtained for the OIT2FOFPIDC over the OT1FPIDC, respectively. Similarly, these reductions in comparison with the OIT2FPIDC are 16.3%, 13.3%, and 12.0%. Also, these reductions, in comparison with the OPIDC, are 33.3%, 27.8%, and 25.8%. Furthermore, simulation results show that the OIT2FOFPIDC is more robust than the other proposed controllers against uncertainties due to structural stiffness. • A new framework controller namely OIT2FOFPIDC is developed for implementation in seismic-excited structures. • Based on the energy concepts, a decentralized optimal design framework of the OIT2FOFPIDC is proposed. • A COA is utilized for optimal tuning of the MFs, scaling factors, and the order of the integral and derivative operators. • The OIT2FOFPIDC represents superior seismic performance and robustness over the proposed OPIDC, OT1FPIDC, and OIT2FPIDC. • The proposed optimal design procedure of the OIT2FOFPIDC can be implemented for a wide range of seismic-excited structures. [ABSTRACT FROM AUTHOR]

This paper is devoted to analyzing Fixed/Preassigned-time synchronization of T–S fuzzy complex networks (TSFCNs) with stochastic effects. Unlike the existing results, partial information communication and complete information communication are all considered according to a Bernoulli distribution. Furthermore, different controllers with quantization are structured to realize our synchronization goal, and one of control parameters can switch based on the error information. Besides, we derive sufficient conditions to guarantee Fixed-time(FDT) and Preassigned-time(PAT) synchronization of TSFCNs, and analyze the difference of FDT and PAT synchronization. Finally, numerical examples and comparisons show that our results are valid. • Both successful communication and unsuccessful communication are considered by using Bernoulli random variable. • Quantized fuzzy controllers are designed, and one of control parameters can switch with the error information of systems. • Sufficient conditions of fixed-time and preassigned-time synchronization are derived for the stochastic T-S fuzzy complex networks. [ABSTRACT FROM AUTHOR]

This paper proposes a control algorithm for controlling the position and attitude of a coaxial rotor drone without knowing the model dynamics. To overcome the major drawback of model-dependent approaches, an optimal model-free fuzzy controller (OMFFC) based on the estimation of the unknown dynamic function of the system is proposed. A time-delay estimation (TDE) technique is effectively exploited to approximate the unknown dynamic function of the system. The estimation error is then offset using a robust adaptive fuzzy logic compensator. Based on Lyapunov stability arguments, the global asymptotic stability of the coaxial rotor drone system is proven. Moreover, a flower pollination-based algorithm is also proposed to generate the optimal parameters to address the trade-off between optimal tracking performance and the design conditions related to the closed-loop stability requirements. The numerical simulations illustrate how the proposed methodology leads to the best performance, as well as less computational complexity compared to the standard proportional–integral–derivative and time-delay estimation-based controllers in the presence of external disturbances. [ABSTRACT FROM AUTHOR]

This paper introduces a novel methodology for optimizing parking recommendations on the campus of Pardubice University, aimed at enhancing the management of existing parking areas. The model takes into account factors such as distance, travel time, parking spot availability, and user preferences, leveraging data processed via the Google Maps API and Open-CV. Fuzzy logic is employed within the model to deal with imprecise concepts, providing adaptability. Performance evaluations yielded an impressive accuracy of 92%, attesting to its viability for real-world implementation. This research significantly advances smart parking solutions, showing promise for reductions in wasted time, alleviated traffic congestion, and improved parking efficiency. [ABSTRACT FROM AUTHOR]

A critical aspect of managing lithium-ion battery packs in electric vehicle applications is accurately determining the State of Charge (SoC). There are several methods available to estimate it, including coulomb counting with direct evaluation, Open circuit voltage, kalman filter with adaptive approach, particle flter, as well as fuzzy logic and data-based approach. In this paper, we use the state of charge data already computed by a data-driven approach and combine it with an ontology of a battery pack. The built ontology models the battery pack, taking into account the topology, types of cells and their organization inside. To make an exact estimation, different strategies of balance control of the cells are considered. SWRL rules are used to compute the state of charge of the whole battery pack. Matlab Simulink multi-physics model of a lithium-ion battery is used to provide simulated data for the experiments. The given model is evaluated based on regression metrics showing its performance. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common type of cancer found in women. One of the most effiective methods for early identification of breast cancer is the mammogram. Numerous computer-aided systems for detecting breast cancer from mammograms have been introduced. In this paper, we present a new way for combining Cellular Automata with Fuzzy Logic, resulting in a so-called Fuzzy Cellular Automaton. The results obtained by testing our proposed approach on the mini-MIAS dataset are close to the ground truth, which is highly encouraging. The choice of using fuzzy logic provides a more flexible technique for categorizing the pixels of interest. The suggested method produces promising results for segmenting the mass region in mammograms with an accuracy of 98,66%, according to the experimental results. [ABSTRACT FROM AUTHOR]

• A novel model of the angle loop error system is constructed for the first time. • The disturbance-observer is introduced into backstepping methodology for hot strip finishing mill. • A novel event-triggered controller is designed in hot strip finishing mill, which cuts down the redundant control actions. • Only an adaptive parameter is employed, which can reduce the computational complexity greatly. In this paper, a disturbance observer-based adaptive event-triggered control scheme is proposed for hot strip finishing mill against the problem brought by the interstand tension and the looper angle. Firstly, a standard backstepping method is adopted to the design of control scheme with the consideration that the interstand looper possesses particular structure of the dynamic model. Then, via selecting a suitable gain function, the unknown disturbance can be estimated well by the disturbance observer. Meanwhile, it is noted that the looper current control has very fast dynamics when generating the looper torque. Therefore, the actuator torque can be viewed as directly related to the control signal. In addition, since the strip tension fluctuation has an effect on width and thickness, it will be dampened until larger than looper angle fluctuation. Furthermore, unknown nonlinear terms generated by the process of finishing mills can be approximated by linear functions integrated by fuzzy logic systems. The proposed event-triggered control scheme is used to guarantee the Lyapunov stability and performance requirements of the overall system. The effectiveness of the proposed controller is illustrated in simulation results and application. [ABSTRACT FROM AUTHOR]

This paper designs a fuzzy adaptive control approach for the Continuous Stirred Tank Reactors (CSTRs) with full state constraints and actuator faults. Using the backstepping design technique and Integral barrier Lyapunov function (IBLF), the full state constraints are handled in the design process of the adaptive controller. Besides, there exist unknown internal dynamics in the CSTRs system, which are identified by the fuzzy logic system. It is shown that the constraints are not overstepped and the CSTRs operate reliably is ensured, despite the presence of system nonlinearities, stuck faults and loss of control effectiveness. Based on the Lyapunov stability theory, the proposed approach can ensure that all the signals of the CSTRs system are globally bounded. Finally, the simulation results on CSTRs are showed to reveal the availability of the developed control scheme. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is widely used in solar applications. Adaptive Neural Fuzzy Inference System (ANFIS) principle is one of the intelligent techniques that is sufficient to be used in control systems. This paper proposes two new efficient intelligent solar tracking control systems based on ANFIS principle. The aim of this paper is to design and implement efficient single and dual-axis solar tracking control systems that can increase the performance of solar trackers, predict the trajectory of the sun across the sky accurately, and minimize the error, therefore, maximize the energy output of solar tracking systems. Experimental data are used to train and test the proposed solar tracking controllers by using month, day and time as input variables to predict the optimum positions for solar tracking systems (tilt/orientation angles). The proposed ANFIS models have been evaluated to find its capability and robustness in tracking the optimum angles that gain the maximum solar radiation. It is found that the proposed controllers are optimum to control solar tracking systems with high prediction rate and the low error rate. Besides, the selected variables along with the selected architecture could successfully predict the optimum tilt and orientation angles. The proposed models provide superior results with five membership functions, and it could obtain high performance for both single-axis and dual-axis solar tracking systems. [ABSTRACT FROM AUTHOR]

Traffic congestion is a major concern for many metropolises. Although it is difficult to regulate traffic flow because of numerous complexities and uncertainties, the traffic congestion problem must be mitigated in order to reduce the environmental problems related to traffic and the time lost on the roads in big cities. Intelligent traffic control methods, the use of which is increasing with the development of new methods, as opposed to conventional methods, and provide more efficient solutions, especially in traffic intersections with high traffic density. In this paper, we propose a new agent-based Fuzzy Logic assisted traffic light signal timing for traffic intersections. Deep Q-Learning algorithms and Fuzzy Logic Control (FLC) are used together in the proposed method. In this study, the proposed method and many traffic light control methods in the literature were simulated. In order to demonstrate the effectiveness of the proposed method, some of the important metrics of evaluation such as traffic congestion, air pollution, and waiting time were used in the assessment of the simulation results. In addition, with the proposed method, it has been shown that the stability and robustness of the system are increased. [ABSTRACT FROM AUTHOR]

• Developing a new optimization model for designing a tire closed-loop supply chain. • Presenting a Spherical fuzzy logic method to calculate the importance of suppliers. • Discussing the application of the model in Greater Toronto Area, Canada. • Solving the multi-objective model using the augmented ε -constraint method. • Performing extensive sensitivity analyses and analyzing the results. For tire manufacturers to remain profitable while fulfilling environmental and social obligations such as producer responsibility, the opportunity lies in designing a tire Closed-Loop Supply Chain (CLSC) which combines forward and reverse supply chains. In this paper, a new multi-objective mixed-integer linear programming model is proposed to configure and optimize a multi-echelon, multi-product, multi-period tire CLSC network based on multiple recovery options and markets. For one of the objectives of the model, the weighting factors (importance) of suppliers are determined according to a unique framework of qualitative criteria. In this respect, a novel decision-making method based on Spherical fuzzy logic is developed. Finally, the solution approach is devised based on the formulation of the augmented ε -constraint method for finding efficient solutions. The application of the model is illustrated focusing on the region of Greater Toronto Area in Ontario, Canada. The optimal quantities for the flows of products, and number and locations of open facilities of the network are computed. The results show that the selected suppliers and allocated orders from them are impacted by considering multiple objectives. [ABSTRACT FROM AUTHOR]

The objective of this paper is to propose a quantitative methodology for the management of work teams through fuzzy logic and analytical network processing (ANP) for managerial decision-making in companies in the small and medium-sized business sectors. Through bibliographic research, the most influential variables were identified. The occurrences of these events were determined by the environment and their respective relevant variables for inclusion in the model. Qualitative data were transformed into quantitative data through fuzzy logic. The interactions of the variables among themselves in ANP were evaluated to estimate the best work teams or the members of each of them. [ABSTRACT FROM AUTHOR]

The dynamic output feedback H ∞ (DOFH) control under imperfect premise matching (IPM) is studied in this paper for continuous-time Takagi–Sugeno (T–S) fuzzy systems. Different from the existing results, the DOFH switching controller, which enjoys membership functions (MFs) distinct from the fuzzy systems, is designed. First, the non-quadratic Lyapunov function (NQLF) based on MFs is utilized to design the controller. The time derivatives of MFs are addressed by the switching strategy. Second, a method based on linear matrix inequality (LMI) is given to make the controller gains solvable. Third, an improved method is developed to incorporate the more boundary information of MFs into the stability conditions to reduce conservatism. Finally, three examples are used to certify the advantage of the approach. • A new fuzzy dynamic output feedback switching controller is developed via non-quadratic Lyapunov function. • The proposed controller enjoys the membership functions different from that of the fuzzy systems. • The new sufficient conditions for the existence of H ∞ controller. • An improved method is proposed to use the boundary information of membership functions. [ABSTRACT FROM AUTHOR]

The present research used a fuzzy logic approach to develop a model for assessing organizational change readiness. Based on an in-depth literature review, a conceptual model for change readiness was developed with four enablers, 13 criteria, and 51 attributes. To address the ambiguity in change readiness assessment, the fuzzy logic theory was used to calculate two core indexes: the fuzzy organizational change readiness index (FOCRI) and the fuzzy performance importance index (FPII). These indexes are computed to identify the level of organizational readiness for change and its weak attributes, therefore allowing managers to formulate a plan for the improvement of change readiness within their organizations. The steps to be followed for assessing change readiness are presented in this paper. Also, a case study was used to illustrate the application of this model in the case of a change project that aimed to implement a project management methodology within a Moroccan construction company. [ABSTRACT FROM AUTHOR]

Until now, rotary draw bending processes have been made error-free by adjustment attempts by experienced machine operators. If corrections are made to prevent wrinkles, there is a risk that the process will lead to cracking. This paper presents a fuzzy controller that independently prevents wrinkling during the tube bending process. Furthermore, measurement methods for crack detection are presented, which will also be integrated into a controller in the next step. The aim is to develop a multi-criteria controller that takes into account quality characteristics such as wrinkling, cracking, spring back and profile elongation. [ABSTRACT FROM AUTHOR]

This paper presents real-time parameter updates that help to make adaptive machining process robust to changes in exogenous operating conditions. This work supports the commercial imperative for a customizable 'batch size of one' implant that does not unreasonably affect operating costs like tool life. A Neural Network (NN) is presented for use within a Computer Numerical Control (CNC) manufacturing cell to determine real-time tool offset adjustments in knee prostheses. This study integrates additional sensor inputs from the CNC cell, including improved force monitoring data for critical tools used in the machining of a Tibial component. The resulting time series force model is used to compare classification performance on Random Forest (RF) and Bi-directional Long Short Term Memory (BiLSTM) Neural Networks. In relation to network training performance, pre-processing improvements are reported for the RF algorithm using a time series data conversion process step. For the case of the BiLSTM model, 2D time series data is converted to a 3D array using a novel projection technique. The accuracy of both of methods is assessed in real-time using tool offset control applied using an adaptive fuzzy logic law. In this use case it has been observed that a RF approach exhibits less overfitting than its BiLSTM counterpart and therefore is a better choice for the dynamic computation of the tool offset height, the actuator output. [ABSTRACT FROM AUTHOR]

One of the critical issues in detecting depression is using facial expressions with image data classification. In This research paper, we proposed Fusion Fuzzy Logic((FFL) with deep learning for identifying depressed people based on their facial expressions. Our proposed model the based on an advanced fuzzy algorithm with deep learning for unordered fuzzy rule(FR) initiation to offer appropriate and suitable opinions based on depressed people's facial expressions(FE), to allow Depression Recognition(DR) from image files and recorded video files. The primary goal of this research work was to use the fusion method to turn these facial expressions (FE) into the detection of depressed states. To elevate the performance of the Fusion Fuzzy Logic((FFL) (fuzzy logic and CNN)), delivering them entreated them several times to imitate specific facial expressions. Our proposed FFL with the CNN model produces exact and dependable results with a 94.3% overall accuracy comparable to human recognition. [ABSTRACT FROM AUTHOR]

In this paper, we develop a smart navigation aid system for blind and visually impaired people (BVIP). The proposed design revolves around a decision support system based on fuzzy logic, a Raspberry Pi4 board for real-time processing, a set of high-performance sensors, and a haptic voice interface to guide the user. The control architecture is based on the Robot Operating System (ROS) which takes care of connecting all the different nodes of the system and generates the decision in the form of a voice haptic message. A security assessment system is implemented using sensor data fusion and a fuzzy classifier to determine the human security path. Experimental tests carried out by BVIP in different environments have shown the effectiveness of the developed solution. [ABSTRACT FROM AUTHOR]

With the advancement in the Internet of Medical Things (IoMT) infrastructure, network security issues have become a serious concern for hospitals and medical facilities. For this, a variety of customized network security tools and frameworks are used to distract several generalized attacks such as botnet-based distributed denial of services attacks (DDoS) and zero-day network attacks. Thus, it becomes difficult to operate routine IoMT services and tasks in between the under-attack scenario. This paper discusses a novel approach named Duo-Secure IoMT framework that uses multi-modal sensory signals' data to differentiate the attack pattern and routine IoMT devices' data. The proposed model uses a combination of two techniques such as dynamic Fuzzy C-Means clustering along with customized Bi-LSTM technique that processes sensory medical data securely along with identifying attack patterns within the IoMT network. As a case study, we are using a dataset to evaluate heart disease which consists of 36 attributes and 18940 instances. The performance evaluation shows that the proposed model evaluates a) prediction of heart issues and b) identification of network malware with an individual accuracy of 92.95% and multi-modal joint accuracy of 89.67% in the IoMT-based distributed network environment. [ABSTRACT FROM AUTHOR]