Optimal Geno-Fuzzy Lateral Control of Powered Parachute Flying Vehicles

In this work, we propose a systematic procedure to design a fuzzy logic controller (FLC) to control the lateral motion of powered parachute (PPC) flying vehicles. The design process does not require knowing the details of vehicle dynamics. Moreover, the physical constraints of the system, such as the maximum error of the yaw angle and the maximum allowed steering angle, are naturally included in the designed controller. The effectiveness of the proposed controller was assessed using the nonlinear six degrees of freedom (6DOF) mathematical model of the PPC. The genetic algorithm (GA) optimization technique was used to optimize the distribution of the fuzzy membership functions in order to improve the performance of the suggested controller. The robustness of the proposed controller was evaluated by changing the values of the parafoil aerodynamic coefficients and the initial flight conditions.

Risk of infestations by Dendroctonus mexicanus Hopkins and Dendroctonus frontalis Zimmermann bark beetles in forests of Michoacán

Introduction: Severe Dendroctonus spp. infestations are reported in North and Central America. Dendroctonus mexicanus Hopkins and Dendroctonus frontalis Zimmermann are recognized as forest pests and are common in the state of Michoacán, Mexico.Objective: To model current and future (2015-2039) spatial distribution of risk D. mexicanus and D. frontalis infestation in forests of Michoacán, Mexico.Materials and methods: Multicriteria evaluation techniques, including the analytic hierarchy process and fuzzy membership functions, were combined with climate and biophysical variables to obtain forest infestation risk maps for D. mexicanus and D. frontalis under current and future climate scenarios.Results and discussion: Climate, fire, tree density and topography were identified as relevant criteria influencing bark beetle outbreaks. The maximum risk value estimated for D. mexicanus was 0.78 and 0.83 for the current and future scenarios, respectively; for D. frontalis these values correspond to 0.84 and 0.85, respectively. In terms of area, high risk of infestation by D. mexicanus increased from 3.9 % (current scenario) to 5.0 % (future scenario); for D. frontalis it decreased from 10.8 % to 9.6 %. The very high-risk value remained constant (0.35 %) for both species and scenarios.Conclusions: Forests of the Transversal Volcanic Belt (in the northeastern part of Michoacán) have the highest risk of bark beetle infestation in the two modeled scenarios.

A Fuzzy Logic Approach for Image Restoration and Content Preserving

Image filtering, which removes or reduces noises from the contaminated images, is an important task in image processing. This paper presents a novel approach to the problem of noise reduction for gray-scale images. The proposed technique is able to remove the noise component, while adapting itself to the local noise intensity. In this way, the proposed algorithm can be considered as a modification of the median filter driven by fuzzy membership functions. Experimental results are compared to static median filter by numerical measures and visual inspection. As was expected, the new filter shows better performances.

A Novel Approach to Generate Type-1 Fuzzy Triangular and Trapezoidal Membership Functions to Improve the Classification Accuracy

Fuzzy logic is an approach that reflects human thinking and decision making by handling uncertainty and vagueness using fuzzy membership functions. When a human is engaged in the design of a fuzzy system, symmetric properties are naturally preferred. Fuzzy c-means clustering is a clustering algorithm that can cluster datasets to produce membership matrix and cluster centers, which results in generating type-1 fuzzy membership functions. However, fuzzy c-means algorithm has a limitation of producing only a single membership function type, Gaussian MF. Generation of multiple fuzzy membership functions is of immense importance as it provides more efficient and optimal solutions to a problem. Therefore, an approach to generate multiple type-1 fuzzy membership functions through fuzzy c-means is required for the optimal and improved results of classification datasets. Hence, to overcome the limitation of the fuzzy c-means algorithm, an approach for the generation of type-1 fuzzy triangular and trapezoidal membership function through fuzzy c-means is considered in this study. The approach is used to calculate and enhance the accuracy of classification datasets called iris, banknote authentication, blood transfusion, and Haberman’s survival. The proposed approach of generating MFs using FCM produce asymmetric MFs, whose results are compared with the MFs produced from grid partitioning (GP), which are symmetric MFs. The results show that the proposed approach of generating type-1 fuzzy membership function through fuzzy c-means is effective and can be adopted.

Bi-Objective Reliability-Cost Interactive Optimization Model for Series-Parallel System

The paper aims are to determine the bi-objective reliability-cost problem of a series-parallel system by employing an interactive approach. Multi-objective optimization is a design methodology that optimizes a combination of objective functions orderly and concurrently. The fuzzy membership functions have been designated to settle the contrary nature of the objectives. Based on these functions and the moment of the objectives in the form of the weight vector, a crisp optimization design is formed. Lastly, the inherited problem is determined with the aid of the PSO (Particle Swarm Optimization) algorithm and confronted with the genetic algorithm. The solution resembling the various choices of the decision-makers towards the evaluation of their decision are listed. A decision-maker can pick an immeasurable one according to his requirement to reach at the aspired goal.

Using Artificial Intelligence to Predict Class Loyalty and Plagiarism in Students in an Online Blended Programming Course during the COVID-19 Pandemic

During the COVID-19 epidemic, most programming courses were revised to distance learning. However, many problems occurred, such as students pretending to be actively learning while actually being absent and students engaging in plagiarism. In most existing systems, obtaining status updates on the progress of a student’s learning is hard. In this paper, we first define the term “class loyalty”, which means that a student studies hard and is willing to learn without using any tricks. Then, we propose a novel method combined with the parsing trees of program codes and the fuzzy membership function to detect plagiarism. Additionally, the fuzzy membership functions combined with a convolution neural network (CNN) are used to predict which students obtain high scores and high class loyalty. Two hundred and twenty-six students were involved in the experiments. The dataset was randomly separated into the training datasets and the test datasets for twenty runs. The average accuracies of the experiment in predicting which students obtain high scores using the fuzzy membership function combined with a CNN and using the duration and number of actions are 93.34% and 92.62%. The average accuracies of the experiment in predicting which students have high class loyalty are 95.00% and 92.74%. Both experiments show that our proposed method not only can detect plagiarism but also can be used to detect which students are diligent.

Fuzzy membership functions tuning for speed controller of induction motor drive: performance improvement

<p>Fuzzy <span>logic controller (FLC) has gained high interest in the field of speed control of machine drives in both academic and industrial communities. This is due to the features of FLC of handling non-linearity and variations. FLC system consists of three main elements: scaling factors (SFs), membership functions (MFs), and rule-base. Fuzzy MFs can be designed with different types and sizes. For induction motor (IM) speed control, (3x3), (5x5) and (7x7) MFs are the most used MFs sizes, and normally designed based on symmetrical distribution. However, changing the width and peak position of MFs design enhance the performance. In this paper, tuning of MFs of FLC speed control of IM drives is considered. Considering (3x3), (5x5) and (7x7) MFs sizes, the widths and peak positions of these MFs are asymmetrically distributed to improve the performance of IM drive. Based on these MFs sizes, the widths and peak positions are moved toward the origin (zero), negative and positive side that produces a controller less sensitive to the small error variations. Based on simulation and performance evaluations, improvement of 5% in settling time (Ts), 0.5% in rise time and 20% of steady-state improvement achieved with the tuned MFs compared to original </span>MFs.</p>

Novel assessment and prediction method for vessel traffic risk degree

To assess the current risk degree and predict the future risk degree of vessel traffic, a novel method is put forward in this study. Different from the existing literature, the available evidence of vessel traffic is directly transformed into the weighted basic probabilistic assignment (BPA) based on the optimal solution to the intersection of fuzzy membership functions in the framework of D-S evidence theory. The matrix deformation algorithm towards the combination rule makes the time complexity low in the process of the risk degree assessment. With respect to the risk degree prediction, the required Sigma points are effectively extracted. We derive the adaptive filtering gain that is suitable for the rapidly changing BPA. Finally, the experiments of vessel traffic in the Dalin Bay are made to indicate performance of the proposed method.

A Novel GIS-Based Approach to Assessment Suitable Irrigation Water Using a fuzzy-Multi Indices Method in Astaneh-Kuchesfahan Plain, Iran

Increasing soil salinity decreased soil permeability and reduced water absorption by plant roots leading to reduced agricultural productivity. For this reason, water quality must be tested before it can be used for agricultural purposes. Accordingly, the currentresearch aimed to assess suitable irrigation water (IW)using a new GIS-basedapproach in Astaneh-Kuchesfahan plain, Iran.Fuzzy logic (FL) via GISwas used to reduce the uncertainty. Four steps were performed to receive this aim. In step 1, the values of nine indices used for agricultural water quality classification were calculated based on chemical analysis of 19 water samples in wet and dry seasons.In step 2, these indices were interpolated via ArcGIS 10.8 software. In the following, fuzzy membership functions (FMF) were used for the standardization of parameters in step 3. Finally, in step 4, foraggregation of the indices, several fuzzy overlay operations were used. Eventually, to identify the most accurate overlay operation,the correlations between the fuzzy memberships and operation maps were used.The results showed that the sum of absolute values for correlations (SAVC) in the dry season is higher than in the wet season.The results also showed that the "GAMMA 0.9" and"GAMMA 0.95"withthe highest SAVCare the bestoverlay operations in dry and wet seasons, respectively. According to the best operation maps, only a small southeast area has"good" groundwater quality for IWin both dry and wet seasons.

COLREGs Compliant Fuzzy-Based Collision Avoidance System for Multiple Ship Encounters

As the number of ships for marine transportation increases with the advancement of global trade, encountering multiple ships in marine traffic becomes common. This situation raises the risk of collision of the ships; hence, this paper proposes a novel Fuzzy-logic based intelligent conflict detection and resolution algorithm, where the collision courses and possible avoiding actions are analysed by considering ship motion dynamics and the input and output fuzzy membership functions are derived. As a conflict detection module, the Collision Risk (CR) is measured for each ship by using a scaled nondimensional Distance to the Closest Point of Approach (DCPA) and Time to the Closest Point of Approach (TCPA) as inputs. Afterwards, the decisions for collision avoidance are made based on the calculated CR, encountering angle and relative angle of each ship measured from others. In this regard, the rules for the Fuzzy interface system are defined in accordance with the COLREGs, and the whole system is implemented on the MATLAB Simulink platform. In addition, to deal with the multiple ship encounters, the paper proposes a unique maximum-course and minimum-speed change approach for decision making, which has been found to be efficient to solve Imazu problems, and other complicated multiple-ship encounters.