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Due to its remote control and capacity to operate in dangerous environments, unmanned aerial vehicles (UAVs) are becoming more popular in human and industrial applications. UAVs are widely used (i.e., military, civilian, industry, and organization). Multirotor and fixed-wing UAVs exist. Multirotor UAVs can hover, conduct vertical takeoffs, and land in tight spaces. Fixed-wing UAVs cover more ground in one flight or battery charge. Fixed-wing UAVs fly longer than multirotors. Fixed-wing UAVs need longer runways for takeoff and landing. However, external disturbances may readily destabilize fixed-wing UAVs (i.e., wind gusts and extreme weather conditions). UAV stability requires a control system. This study examines the Cessna182 UAV model. Fuzzy-PD controls are explored and built to handle disturbances that impact UAV dynamics. Fuzzy-PD controllers combine fuzzy logic and PD control benefits. Fuzzy logic determines PD controller adaptive controller gains. The fuzzy-PD controller's fuzzy rules-base improves system responsiveness, settling time, overshoot, steady-state error, and rising time. MATLAB/Simulink® simulates Fuzzy-PD controllers. Fuzzy-PD control improves Cessna182 fixed-wing UAV longitudinal stability. The controller minimizes system settling time, overshoot, and steady-state error, and its resilience reduces wind impacts on UAV stability. Industrial controllers can operate UAVs better than commercial ones. [ABSTRACT FROM AUTHOR]

A passenger-bus represents one of the vehicle types that can cause serious damage during the accident. Its frontal crashworthiness may threaten substantial damage to life and property. This is because the design of a bus structure does not meet frontal crashworthiness standards and regulations. Currently, the design of a bus structure and manufacturing in Thailand only focuses on the overturned test. The front crash standard have not been yet considered as structural requirements. This paper introduces a novel design of a passenger-bus considering the frontal crash based on the standard of United Nations Economic Commission for Europe Regulation No. 29 (UN ECE-R29). A simulation model was done to determine the structural strength against the produced load considering a full-frontal impact. Finite element analysis was applied to evaluate frontal impacts. As a result, the energy absorption corresponding to the UN ECE-R29 regulation is 55 kJ, while the test model structure could absorb about 52.4 kJ of energy. In fact, the total deformation from the UN ECE-R29 signifies the severe deformation in the frontal structures, which caused the significant damage to the driver dummy. Clearly, reinforcement of the thin-walled tubular construction could improve a bus's structural strength. Consequently, the driver dummy is not affected by the structure's distortion, enhancing the safety and reliability standards for passenger-buses. [ABSTRACT FROM AUTHOR]

Based on fiber and matrix type, structure, and manufacture, fiber-reinforced composites are quickly expanding to serve current industrial applications. Composite materials have demonstrated exceptional conductivity, recyclability, density, and strength-to-weight ratio. As a result, much research is necessary before selecting appropriate composites. The purpose of this study is to conduct a thorough evaluation of the mechanical characteristics of carbon fiber/epoxy and flax fiber/epoxy composites. RVE creates three representative volume elements to determine the stiffness of composite materials using the finite element method. Based on the unidirectional configurations, the longitudinal modulus, transverse modulus, shear modulus, and Poisson's ratio are thoroughly examined. To analyze the composite qualities, theoretical methodologies such as the rule of mixing, Chamis, Halpin-Tsai, and Nielsen theorems are used, and the findings are compared to those of FEM solutions. As a result, the elastic characteristics of carbon fiber/epoxy and flax fiber/epoxy may rise when the fiber volume percentage increases. The analytical findings further show that the square form of the RVE unidirectional model corresponds to the Chamis theorem better than alternative theoretical solutions. [ABSTRACT FROM AUTHOR]

Due to favorable strength-to-weight ratio properties, composite materials are widely used in various industrial applications. The manufacturing process, type of fiber, type of matrix, fiber orientation, number of laminations, and volume fraction can significantly affect the mechanical properties of composite materials. Thus, experimental and simulation techniques are required to investigate such material properties. Usually, the strength-to-weight ratio is a crucial factor for analyzing material properties. However, new carbon fiber/epoxy composites may require additional manufacturing with higher costs. To deal with manufacturing costs, an artificial neural network (ANN) is used to predict the flexural strength of such composite materials. ANN training data are the weight percentage, width, manufacturing, ply orientation, and thickness of a graphite filler. To evaluate the desired output with flexural strength, an ANN structure is constructed using a multi-layer feed-forward neural network. ANN mathematics and computing are also compared with Levenberg– Marquardt backpropagation (LMBP) and a scaling conjugate gradient (SCG) to determine the differences between the predicted and actual results. Correlation coefficients for flexural strength represent a range between 0.9791 and 0.9926. Regression analysis signifies an acceptable value of about 0.95, while the mean squared error (MSE) is less than 0.01. Therefore, it can be concluded that the proposed method-based on LMBP is more accurate than the SCG. Numerical results show that the proposed method-based ANN is effective and useful for designing flexural strength of composite materials. [ABSTRACT FROM AUTHOR]

This research aimed to investigate the effect of substance concentration on the spectral information of a multi-spectral sensor. The measurement unit contained an Arduino UNO microcontroller that was used to read the spectral data obtained from a multi-spectral sensor. The multi-spectral sensor received reflected light from the samples, which were coffee phantoms with various coffee concentrations in the range of 0.5 – 60 mg/dl. Normalized spectral data were analyzed using analysis of variance and regression methods to investigate the effect of coffee concentration on the normalized spectral data of the multi-spectral sensor. According to the results of the study, the coffee concentration is related to the normalized data in the form of a polynomial function. The normalized spectral data inversely varied with the coffee concentration. The quadratic equation obtained from the regression at wavelengths of 580–600 nm was selected as the prediction model for measuring the coffee concentration. The predicted concentrations agreed with the actual concentrations. Therefore, spectral sensors can be applied to the concentration measurement of some colored biological substances. [ABSTRACT FROM AUTHOR]