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A nanofluid (Nf), which contains tungsten trioxide, WO3, nanoparticles as solid-particles distributed in liquid paraffin, is produced. A set-up is provided to experimentally measure the thermal conductivity (TC) of this mixture at various mass fractions and temperatures. Further, two universal approximators of fuzzy logic (FL) and artificial neural network (ANN) are presented to predict the TC of this mixture. The present work's novelty can be presented as: prepare a WO3/paraffin nanofluid, measure it's TC, and develop a novel statistical/numerical model of FL compared with ANN. As it can be seen, temperature and concentration effects positively on nanofluid TC. Moreover, ANN and fuzzy models show suitable precisions; however, the interpolation capability of the proposed fuzzy model outperforms ANN at non-trained inputs. [ABSTRACT FROM AUTHOR]

This paper proposes a new switching adaptive fuzzy controller and applies it to vibration control of a vehicle seat suspension equipped with a semi-active magnetorheological (MR) damper. The proposed control system consists of three functioned filters: (1) Filter 1: a model of interval type 2 fuzzy to compensate disturbances; (2) Filter 2: a 'switching term' to evaluate the magnitude of disturbance; and (3) Filter 3: a group of adaptation laws to enhance the robustness of control input. These filters play a role of powerful shields to improve control performance and guarantee the stability of the applied system subjected to external disturbances. After embedding a PID (proportional-integral-derivative) model into Riccati-like equation, main control parameters are updated based on the adaptation laws. The proposed controller is then synthesized in two different cases: high disturbance and small disturbance. For the high disturbance, a special type of sliding surface function, which relates to an exponential function and its t-norm, is used to increase the energy of control system. For the small disturbance, the energy from the modified t-norm of the sliding surface is neglected to reduce the energy consumption with maintaining the desired performance. To demonstrate the effectiveness of the proposed controller, a vehicle seat suspension installed with controllable MR damper is adopted to reflect the robustness against external disturbances corresponding to road excitations. It is validated from computer simulation that the proposed controller can provide better vibration control performance than other existing robust controllers showing excellent control stability with well-reduced displacement and velocity at the position of the seat. [ABSTRACT FROM AUTHOR]

With the advancement of the marketization process, inventory management has transformed from a single backup protection function to an essential function for enterprises, which helps to survive and develop. Inventory control in supply chain management is the important content of supply chain management. The new management mode makes inventory management present many new characteristics and problems compared with traditional inventory management. From the view of system theory and integration theory, it is imperative to re-examine the problem of inventory control, put forward new inventory management strategies adapted to integrated supply chain management, and improve the integration of the whole supply chain, which can enhance the agility and market response speed of enterprises. Based on the in-depth study of the joint inventory management model, this paper analyzed the current situation of the joint inventory management to optimize the inventory. In view of the achievements and shortcomings of the current research, a more systematic and improved optimization model of the supply chain inventory was proposed by using the basic ideas of ant colony algorithm and fuzzy model.

The departments of transportation worldwide are facing various challenges despite introducing and incorporating various vehicular features. One of such challenges is to make vehicles autonomous, intelligent, and capable of self-learning to evolve their knowledge repository. In this paper, human cognition is proposed to be implemented in vehicles so that they can perform human-like decisions. Therefore, the process of vehicular route decision is debated cognitively in order to provide route information intelligently. The in-vehicle routes provided by the GPS are not optimal and lack on-demand user requirements. GPS connectivity issues, in certain conditions, make it difficult for vehicles to take real-time decisions. This leads to the idea of self-decision by the vehicle controller. We propose a cognitive framework for vehicles to make self-decisions that use cognitive memory for storing route experiences. The framework strengthens the existing in-vehicle route finding capability and its provision in a more realistic manner. The user is provided with all available route-related information that is required for the journey. In addition, the route episodes are learned, stored, and accessed inside the cognitive memory for an optimal route provision. The vehicle learns about the routes and matures with route-experience by itself with the passage of time. In simulations, fuzzy modeling is used to validate the impact of cognitive parameters over static/conventional parameters. Moreover, artificial neural networks are used to minimize the error rate in learning to achieve cognitive route decisions. The proposed in-vehicle cognitive framework outperforms the existing route provision system that is inadequate and provokes the user's anxieties during driving. Besides, the proposed scheme gradually gets mature in delivering optimal as well as latest route-related information.

Background & Aim: One of the vital consequences of the expedite growth of urbanization and the physical development of the cities of the country has been the disruption of the distribution system of the service centers in recent decades, causing the social inequality of citizens in accessing such services. Urban public services structure the citychr('39')s physical, social and spatial nature; thus, irreparable effects are caused on the structure, the nature of the city and the class segregation of the urban neighborhoods due to injustice in its distribution, and faces urban management with serious challenges. The extension of the concepts of justice in the field of geography and urban literature, which began approximately 40 years ago, has recently led to new approaches in the field of epistemology. This study examines the spatial distribution of urban services in Mashhad metropolis with the aim of measuring spatial justice. Methodology: The present research is applied in terms of purpose and descriptive-analytical in terms of method. The data collection method is documentary and as a desk study and the technique used is a fuzzy method. Statistics and research indices were the distributions status of service use intervals at the level of 13 districts of Mashhad metropolis, obtained from a detailed plan. By measuring the research indices, the present research attempts to compare and classify the different districts of Mashhad in terms of the enjoyment rate of the access index, in order to provide a proper route for balancing the city development pillars. Findings: The analysis results indicated that approximately 10,381 hectares, equivalent to 30% of the city area suffers inadequate and relatively inadequate quality in accessing a variety of services, meanwhile districts 1, 11, and Samen were more appropriate than other districts. Also, districts 7, 6, and 2 lack the minimum zones with very good access to the services

Every year we see natural disasters causing major financial and human losses to human societies, and unfortunately, our country also has a bitter taste of it every now and then. Floods, earthquakes, droughts, frostbites, storms, and so on ... There are a lot of financial and financial losses to the country, which will require measures to reduce the damage caused by these disasters. Definitely, the first step is to understand and understand these phenomena. The development of the process of urbanization and degradation of vegetation, soil erosion, and global climate change has already paid attention to the importance of addressing the issue of the urban flood. This research was carried out to determine the risk of flooding in Sari using multi-criteria decision-making techniques. Using nine criteria, the distance from the river, runoff coefficient, CN coefficient, population density, residential density, slope, land use, the age of the building and outdoor space were developed. By preparing the required layers, determining the weight of each layer based on their importance in the occurrence of a flood. After the final weighing, the layers were compared in two to two by Expert Choice software and the matrices derived from these comparisons were transferred to Idrisi software and the final coefficient was determined for each layer. Finally, by applying these coefficients, ArcGIS software provided a flood risk zoning map in the city of Sari. The results show that flood risk in the center and south of the city has been the highest. Flood risk zoning map shows that 12.24% of the map area is in a very high danger zone and 37.05% in very low-risk zoning. To reduce the risk of retrofitting buildings around the river to reduce flood damage.

Purpose. The aim of the article is to develop a mathematical model for a comprehensive assessment of the probability of failure of an overvoltage protection device using a fuzzy set device, which is based on expert knowledge and diagnostic data obtained during the operational period. Methodology. The study was carried out using the expert survey method, weight coefficients method, Saati pair comparison matrix method, fuzzy logic methods, fuzzy sets theory, high voltage technology theory, thermal radiation theory. Findings. The authors developed a fuzzy model for determining the probability of failure of a gate arrester and a voltage limiter, taking into account the influence of a combination of such factors as the state of insulation, the state of current-carrying nonlinear elements, the temperature state of the object under study, and the number of operations of the apparatus. On the basis of the model, an automated software system was developed, using which the state of the voltage limiter type 3ER2 276-2PF32-1NA1 of the «SIEMENS» company was diagnosed. It is operated at Dneprovskaya HPP-1, 330 kV for cell ОПН AT-331, and arresters of type РВМК -750M for cell PB 750 2 AT. Originality. The theory of technical diagnostics of high voltage overvoltage protection devices without disconnecting devices from the network (online) on the basis of a fuzzy model for determining the probability of failure of the device has been further developed. The known model differs from the known ones in that it takes into account the state of insulation, the state of the conductive parts, the number of operations of the apparatus, the thermal state of the apparatus and the contact connections, and allows calculating the probability of failure of the apparatus during its operation. Practical value. The developed mathematical model can be used in automated software and hardware-software systems for: diagnosis, maintenance and repair planning, and distribution of financial assets of electric power enterprises. The results of diagnosing different protection apparatus confirmed the adequacy of the developed model. The developed model can be used for all types of protection devices for high voltage switchgears. The model makes it possible to comprehensively evaluate the technical state of the investigated object by integrating the input parameters (current, resistance, temperature, etc.) that are inherently different in nature and which affect the technical state and the probability of its failure. The constructed model allows the machine to be put out of repair after the current state of the object, and not according to the schedule of repairs, which will save the material and human resource, and taking into account the state course for estimating the equipment resource according to the actual state and creating switchgears without a permanent maintenance staff is very relevant.

Explosive, non-programmed growth of population and non-stop migration to the cities followed by rapid physical development in recent years caused many problems for cities and their inhabitants. The present study investigated the geomorphological status of the city of Sarvabad and assessed its capabilities for the purposes of urban physical development. In order to achieve the research objectives, reviewing the related literature and using experts' opinions were necessary and influential. Geomorphologic, geology, human parameters, including slope, direction of gradient, height, geology, land use, the distance from the fault, its distance from the river, distance from communicative way and distance from the urban boundary were determined. Then their communicative layers were prepared. For this purpose, two methods including fuzzy logic model and ANP were utilized. Firstly, the required layers were gathered and converted into fuzzy method. Then in the ANP model, the value of each layer calculated and network maps were integrated using fuzzy logic and finally presented as a map of the areas susceptible to development. The results confirm that about 25 % of the studied area which is located in South, East and Southeast section of the boundary need to be developed.

In agriculture with 4.0 technologies, developing a decision model with a 0.0 cost is attractive to small farmers. In water management, if this approach could be used to promote sustainability and optimization, it could become a pathway to reach the sustainable development goal in 2030. The core of this work is the development of a 4.0 mathematical model (based on fuzzy concepts) to verify the benefits of the production of lettuce irrigated with magnetically treated water at different replacement rates. This approach is achieved using computational 4.0 software and manual methods. The aim of mathematical modeling is to understand or explain a natural phenomenon associated with a given area of knowledge, and fuzzy-rule-based systems have been widely used in different types of in-depth research. [ABSTRACT FROM AUTHOR]

Supporting the Quality of Service (QoS) for broadcasting techniques in Vehicular Ad hoc NETetworks (VANETs) is a primordial concern. Qualitatively, QoS is an aspect reflecting the network performance, but quantitatively, it is a function of multiple parameters such as end-to-end delay, packet loss ratio and overhead. These parameters are changing over time and according to adopted protocols. Therefore, it is very difficult to estimate with a crisp value the system QoS. Besides, there is a lack of technical tools for modelling, measuring and comparing the level of QoS performed by different broadcasting protocols under different network constraints. This paper proposes FUZZYVAN-QoS a holistic model based on a fuzzy system simplifying this problem and going through discussing its dependency on different Vehicular Ad hoc NETtwork (VANET) services and applications types. Then, we use a case study; applied on multiple VANET broadcasting protocols, to illustrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

In recent years, with the further breakthrough of artificial intelligence theory and technology, as well as the further expansion of the Internet scale, the recognition of human emotions and the necessity for satisfying human psychological needs in future artificial intelligence technology development tendencies have been highlighted, in addition to physical task accomplishment. Musical emotion classification is an important research topic in artificial intelligence. The key premise of realizing music emotion classification is to construct a musical emotion model that conforms to the characteristics of music emotion recognition. Currently, three types of music emotion classification models are available: discrete category, continuous dimensional, and music emotion-specific models. The pleasure-arousal music emotion fuzzy model, which includes a wide range of emotions compared with other models, is selected as the emotional classification system in this study to investigate the influencing factor for musical emotion classification. Two representative emotional attributes, i.e., speed and strength, are used as variables. Based on test experiments involving music and non-music majors combined with questionnaire results, the relationship between music properties and emotional changes under the pleasure-arousal model is revealed quantitatively. [ABSTRACT FROM AUTHOR]

In Hierarchical Wireless Sensor Network (HWSN), the energy transmission of data packets belongs to the distance between source and destination, vulnerable to various malicious attacks. Thus clustering of HWSN reduces energy consumption, achieves scalability, and reduces network traffic. Therefore in this paper, a Multi-level Fuzzy Cluster Trust Estimation (MFCTE) logic model is used for clustering nodes and select trustworthy Cluster Head (CH) from clustered nodes. For this, the proposed method uses five attributes to become a trust- based CH. The following attributes given as input to fuzzy are Density of the other sensor nodes near to CH, Compaction of the surrounding nodes, Distance from the base station, Residual energy of the sensor nodes, and Packet integrity. MFCTE detects malicious nodes and ensures security in CH by automatically adjusting a load of direct trust, indirect trust, and parameters of update mechanism. The simulation results indicate that the proposed technique is energy effcient in terms of energy consumption, network lifetime for different network sizes, and better at defining malicious attacks. [ABSTRACT FROM AUTHOR]

The construction of the evaluation index system of employee satisfaction level is the basis for an enterprise to measure the level of employee satisfaction. In this paper, the author analyzes the employee incentive in supply chain network based on asymmetric information game analysis and fuzzy model. Through the correction of network weight and threshold value, the error function decreases along the gradient direction. The construction and training of the network can be realized by MATLAB. The newff function in the software is used to construct the network, and the train function is used to train. This evaluation index system is a multi-level target evaluation system based on asymmetric information game, and its basic principle is to simplify complex problems. Long term effort is bigger when the salesperson's risk averse parameter is more than a certain value or risk averse parameter is less but discount factor is more than a threshold; short term effort is bigger when risk averse parameter and discount factor is both less. For both information scenarios, the compensation contracts are designed and comparison analyses are conducted. [ABSTRACT FROM AUTHOR]

Floods are a climatic phenomena that affect different regions worldwide and that produces both human and material losses; for example in 2017, six of the worst floods were the cause of 3.273 deaths worldwide. In Colombia, the strong winter wave presented between 2010 and 2011, caused 1,374 deaths and 1,016 missing persons. The main river in Colombia is the Magdalena, which provides great benefits to the country but is also susceptible to flooding. This article presents a proposal to optimize a fuzzy system to prevent flooding in homes adjacent to areas of risk to the Magdalena River. The method used is based on evolutionary algorithms to perform a global search, including a gradient-based algorithm to improve the solution obtained. The best result achieved was the Mean Square Error (MSE) of 7, 83E - 05. As a conclusion, it is needed to employ optimization methods for the adjustment of parameters of the fuzzy system when considering that the sets and the rules are systematically obtained. [ABSTRACT FROM AUTHOR]

In this work, the problem of predictive control of dissolved oxygen for the activated sludge process model with high nonlinearity and strong coupling is addressed. Firstly, the determination of the structure of fuzzy rules is displayed established upon Activated sludge model 1 (ASM1). Besides, the fuzzy space is divided through the clustering algorithm of fuzzy C-means. The corresponding parameters are estimated by means of the well-known least squares method. Subsequently, a fuzzy predictive model of dissolved oxygen is established by using the historical data. The aim is to design a predictive controller that is capable of performing the online track of dissolved oxygen attributed to better dynamic response and steadier output in different weather. Ultimately, the availability and validity of the developed technique are verified by a comparison example. [ABSTRACT FROM AUTHOR]

The dynamic output feedback control problem with output quantizer is investigated for a class of nonlinear uncertain Takagi‐Sugeno (T‐S) fuzzy systems with multiple time‐varying input delays and unmatched disturbances. The T‐S fuzzy model is employed to approximate the nonlinear uncertain system, and the output space is partitioned into operating regions and interpolation regions based on the structural information in the fuzzy rules. The output quantizer is introduced for the controller design, and the dynamic output feedback controller with output quantizer is constructed based on the T‐S fuzzy model. Stability conditions in the form of linear matrix inequalities are derived by introducing the S‐procedure, such that the closed‐loop system is stable and the solutions converge to a ball. The control design conditions are relaxed and design flexibility is enhanced because of the developed controller. By introducing the output‐space partition method and S‐procedure, the unmatched regions between the system plant and the controller caused by the quantization errors can be solved in the control design. Finally, simulations are given to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

At present, the teaching of architectural art in China is still relatively traditional, and there are still some problems in the actual teaching. Based on this, this study combines the Naive Bayesian classification algorithm with the fuzzy model to construct a new architectural art teaching model. In teaching, the Naive Bayesian classification algorithm generates only a small number of features for each item in the training set, and it only uses the probability calculated in the mathematical operation to train and classify the item. Moreover, by combining the fuzzy model, the materials needed for architectural art teaching can be quickly generated, and the teaching principles and implementation strategies of architectural art are summarized. In addition, this paper proposes an attribute weighted classification algorithm combining differential evolution algorithm with Naive Bayes. The algorithm assigns weights to each attribute based on the Naive Bayesian classification algorithm and uses differential evolution algorithm to optimize the weights. The research shows that the method proposed in this paper has certain effect on the optimization of architectural art teaching mode. [ABSTRACT FROM AUTHOR]

During the operation process of a system its technical state is changed. The changes take place because of the wearing factors impact. The impact depends on the flow and intensity of the operation process what is characterized by the time histories of the working parameters. Simultaneously, the changes of the technical state are correlated with the changes of the amount of the operational potential included in a system. In order to avoid the inability state occurrence the amount of this potential should be higher than the boundary value. The amount of the operational potential included in a system is determined by the values of the cardinal features of it but in the case of the real technical system the values cannot always be measured. Therefore, the amount of the operational potential and the technical state of the system cannot always be determined online. To solve this problem the model of the operational potential consumption process was created and presented in the paper. The model uses artificial intelligence techniques to calculate the change of the operational potential amount by determining the changes of the cardinal features of the system on the basis of the time histories of the working parameters. The verification of the model quality was performed using the pulverized boiler OP-650k-040 operating in the power plant. The description of the conducted research and the results of the verification were presented in the end of the paper proving the adequacy of the model implementation in the case of industrial objects. [ABSTRACT FROM AUTHOR]

This paper investigates the fault tolerance problem of flexible satellites subject to multiple disturbances and actuator faults. An adaptive fault tolerant control (FTC) approach based on disturbance observer is presented for attitude control system (ACS) with actuator faults, elastic modal, modeling error and environmental disturbance torque in this paper. Different from some existing disturbance observer-based control (DOBC) approaches, Takagi-Sugeno (T-S) fuzzy modeling technology is applied to describe the elastic modal. A fuzzy disturbance observer and a fault diagnosis observer are constructed to estimate the elastic modal and actuator fault, respectively. Then, based on fault accommodation and DOBC, a new adaptive FTC strategy is designed to achieve the anti-disturbance performance and improve the system reliability. Finally, the efficiency of the proposed FTC scheme is verified by simulation results. [ABSTRACT FROM AUTHOR]

During the digital forensic process, different software and hardware tools are used. The devices from which the digital evidences are collected have been varied in parallel with the developments in technology. The issue of identifying the mobile phone owner's friends and assessing his relationship with them with the help of digital evidences collected from the Android mobile phones has been studied in the literature and it is still under investigation. The software developed in this work enables accessing a variety of data that have evidential value in the court proceedings; these include physical and logical acquisition of images from mobile phones with Android operating system, extracting images for investigations, examining different file types in images, and databases. This software can collect and examine the evidences and then produce reports. At the same time, it can identify criminals or people with potentially have connections to those people whose accounts are under investigations by using developed analysis model which examines the relationships between social media applications' data, phone contacts and calling histories collected from the mobile devices. In this work, the evidences examined by using a novel software developed by the authors which performs multiple tasks using a single interface and the corresponding results are presented. [ABSTRACT FROM AUTHOR]

Cloud computing is a new computing paradigm which is gaining wide acceptance among scientific fraternity in the recent years. The services of cloud could be effectively used for running large-scale data and computation-intensive scientific workflow applications. Finding the optimal schedule for such workflows has been a major concern among the cloud users. In the present work, a novel approach of combining both optimization of the schedule along with the allocation of the virtual machines (VMs) based on security requirements is envisaged. This paper focuses on generating an optimized schedule for the complex workflow structures. The main objective of the schedule is to minimize the makespan of the schedule. In this paper, we design the scheduling heuristic based on the cost prediction matrix (CPM) for optimized cost calculation. The CPM will estimate the execution cost by considering the child's child task also. This leads to a prophetic estimation on the available VMs. In addition to this, we have used a fuzzy-based decision model for deciding the selection of the VMs based on security constraints in the cloud. This fuzzy model is combined with the optimized cost calculation from CPM for each and every task of the workflow. The proposed secured cost prediction-based scheduling (SCPS) algorithm then schedules the task in the best possible VM, so that the makespan is minimized. Our results show that the newly developed SCPS algorithm yields efficient schedule compared to other existing scheduling models in spite of the inclusion of security constraints besides scheduling. Nevertheless, this secured scheduling is done without much increase in the time complexity. [ABSTRACT FROM AUTHOR]

This study proposes a novel methodology for the extraction of a hierarchical Takagi-Sugeno fuzzy rule-based architecture from data. This architecture reduces the number and complexity of involved fuzzy rules, and, in many cases, improves the predictive performance of the model. The proposed hierarchical architecture takes the form of a cascading topology in which the predicted result computed at the previous layer is considered in the output part of the fuzzy rules. We propose a well-defined general methodology for the extraction of this hierarchical topology from data and discuss strategies for feature selection and choosing the number of rules at each level. The performance of the proposed methodology is demonstrated through extensive experiments, including case studies outlining specific behaviors and parameterizations, and comparative experiments showing the performance of the proposed architecture compared to a standard flat fuzzy rule-based system. [ABSTRACT FROM AUTHOR]

• Utilization of fuzzy based model to predict methane generation from landfills. • Calibration of the model based on data from 20 landfills in Ontario, Canada. • Validation of the model using data from 10 big landfills in Ontario, Canada. • Comparison of fuzzy based mode with first order models for LFG generation. In this study, multi-criteria assessment technique is used to predict the methane generation from large municipal solid waste landfills in Ontario, Canada. Although a number of properties determine the gas generation from landfills, these parameters are linked with empirical relationships making it difficult to generate precise information concerning gas production. Moreover, available landfill data involve sources of uncertainty and are mostly insufficient. To fully characterize the chemistry of reaction and predict gas generation volumes from landfills, a fuzzy-based model is proposed having seven input parameters. Parameters were identified in a linguistic form and linked by 19 IF-THEN statements. When compared to measured values, results of the fuzzy based model showed good prediction of landfill gas generation rates. Also, when compared to other first order decay and second order decay models like LandGEM, the fuzzy based model showed better results. When plotting the LandGEM and Fuzzy model values to the actual measured data, the fuzzy model resulted in a better fit to actual data than the LandGEM model with a coefficient of determination R2 of 0.951 for fuzzy model versus 0.804 for LandGEM model. The results show how multi-criteria assessment technique can be used in modelling of complicated processes that take place within the landfills and somehow accurately predicting the landfill gas generation rate under different operating conditions. [ABSTRACT FROM AUTHOR]

This paper presents an observer-based control strategy to improve the maneuverability and stability performance of four-wheel independently driven electric vehicles with active front-wheel steering systems. Since the system states are difficult to be measured directly, a novel observer is designed to estimate the vehicle yaw rate and lateral velocity simultaneously. Takagi–Sugeno fuzzy model is used to handle the time-varying parameters of the vehicle model. Based on Lyapunov function theory, stability conditions of the closed-loop system are derived. The fuzzy H ∞ controller is designed to make the resulting T–S fuzzy system asymptotically stable and satisfy H ∞ performance under given constraints. Simulation results are given to demonstrate the validity of the presented control method. [ABSTRACT FROM AUTHOR]

Biodiesel is considered to be a promising alternative option to diesel fuel. The main contribution of the current work is to improve compression ignition engine performance, fueled by several biodiesel blends. Three metrics were used to evaluate the output performance of the compression ignition engine, as follows: brake torque (BT), brake specific fuel consumption (BSFC), and brake thermal efficiency (BTE), by varying two input parameters (engine speed and fuel type). The engine speeds were in the 1200–2400 rpm range. Three biodiesel blends, containing 20 vol.% of vegetable oil and 80 vol.% of pure diesel fuel, were prepared and tested. In all the experiments, pure diesel fuel was employed as a reference for all biodiesel blends. The experimental results revealed the following findings: although all types of biodiesel blends have low calorific value and slightly high viscosity, as compared to pure diesel fuel, there was an improvement in both BT and brake power (BP) outputs. An increase in BSFC by 7.4%, 4.9%, and 2.5% was obtained for palm, sunflower, and corn biodiesel blends, respectively, as compared to that of pure diesel. The BTE of the palm oil biodiesel blend was the lowest among other biodiesel blends. The suggested work strategy includes two stages (modeling and parameter optimization). In the first stage, a robust fuzzy model is created, depending on the experimental results, to simulate the output performance of the compression ignition engine. The particle swarm optimization (PSO) algorithm is used in the second stage to determine the optimal operating parameters. To confirm the distinction of the proposed strategy, the obtained outcomes were compared to those attained by response surface methodology (RSM). The coefficient of determination (R2) and the root-mean-square-error (RMSE) were used as comparison metrics. The average R2 was increased by 27.7% and 29.3% for training and testing, respectively, based on the fuzzy model. Using the proposed strategy in this work (integration between fuzzy logic and PSO) may increase the overall performance of the compression ignition engine by 2.065% and 8.256%, as concluded from the experimental tests and RSM.

In this study, fuzzy logic neural networks were employed to optimize the friction stir welding (FSW) process parameters in the joining of copper plates. The FSW parameters were considered as the input variables, for which micro-hardness, nano-hardness, and yield strength of the joints were the responses. The micro-hardness and nano-hardness were measured by Vickers hardness and nanoindentation tests, respectively. The microstructure and substructure of the joints were evaluated by optical, scanning electron, and orientation imaging microscopes. The optimum process parameters through which the maximum strength was achieved were the tool rotational rate of 560 rpm, tool traverse speed of 175 mm/min, and tool axial force of 2.27 kN. The low heat input joints, owing to the finer grain sizes, high density of dislocations, and larger Taylor factors, indicated greater strength relative to the high input joints. Microstructure characterization revealed that dominant strengthening mechanisms of the joints were dislocation density, texture effect, and grain boundary hardening.

The article presents the results of a study performed and substantiated based on the principles of a new method of diagnostics of technical conditions of a hybrid powertrain regardless of the structural diagram and design features of a hybrid vehicle. The presented new technology of the diagnostics of hybrid powertrains allows an objective complex assessment of their technical condition by diagnostic parameters in contrast to existing diagnostic methods. In the proposed method, a mechanism for the general standardization of diagnostic parameters has been developed as well as for determining the numerical values of the parameters of the powertrain. The control subset was used to control the learning error. As a result of debugging the system, the scatter of experimental and calculated points has decreased, which confirms the quality of debugging the tested fuzzy model. As a result of training the artificial neural network, the standard deviation of the error in the control sample was 0.012·Pk. A symmetry method of diagnostics of the technical state of a hybrid propulsion system was developed based on the concept of a neural network together with a neuro-fuzzy control with an adaptive criteria based on the method of training a neural network with reinforcement. The components of the vector functional include the criteria for control accuracy, the use of traction battery energy, and the degree of toxicity of exhaust gases. It is proposed to use the principle of symmetry of the guaranteed result and the linear inversion of the vector criterion into a supercriterion to determine the technical state of a hybrid powertrain on a set of Pareto-optimal controls under unequal conditions of optimality.

Takagi-Sugeno (T-S) fuzzy coronary artery system. We use the T-S fuzzy model to represent the coronary artery system, because the coronary artery system has complicated nonlinear characteristic in reality. Based on the new model, a fuzzy parametric adaptive output feedback controller is designed to achieve the H∞ synchronization of coronary artery system with input nonlinearity and parameter perturbations. Some simulation results are given to illustrate the effectiveness of our control strategy.

A fuzzy model predictive control scheme based upon adaptive neural network disturbance observer is proposed for the longitudinal dynamics of a constrained hypersonic vehicle (HV) in the presence of diverse disturbances. First, an equivalent disturbed fuzzy dynamic model with the varying parameters is constructed to approximate the nonlinear dynamics, where the inevitable lumped disturbances, including the fuzzy modeling error, extraneous disturbances, and model uncertainties caused by aerodynamic uncertainties, need to be suppressed. Subsequently, according to the parameter-dependent Lyapunov function, the proposed scheme taking the varying parameters into account is developed to explicitly handle the constraints of fuel equivalence ratio, elevator deflection, and angle of attack. Furthermore, based on the strong nonlinear approximation ability of neural network (NN), an adaptive neural network disturbance observer with the adaptive laws of NN's weight matrixes is established to estimate lumped disturbances, and then an additional compensator formulated by integrating the estimations of lumped disturbances and the corresponding compensation gain matrix is appended to the proposed method for suppressing the lumped disturbances directly. Finally, the comparative simulation results for tracking the reference commands of velocity and altitude demonstrate that the proposed method provides a satisfactory tracking performance even when HV is in the presence of lumped disturbances and constraints.