Block backward differentiation formulas for solving second order fuzzy differential equations

In this paper, we study the numerical method for solving second order Fuzzy Differential Equations (FDEs) using Block Backward Differential Formulas (BBDF) under generalized concept of higher-order fuzzy differentiability. Implementation of the method using Newton iteration is discussed. Numerical results obtained by BBDF are presented and compared with Backward Differential Formulas (BDF) and exact solutions. Several numerical examples are provided to illustrate our methods

Laser Surface Structuring of Biocompatible Polymers Films for Potential use in Tissue Engineering Applications

Ph.DDOCTOR OF PHILOSOPH

On the effects of using CO2 and F2 lasers to modify the wettability of a polymeric biomaterial.

Enhancement of the surface properties of a material by means of laser radiation has been amply demonstrated previously. In this work a comparative study for the surface modification of nylon 6,6 has been conducted in order to vary the wettability characteristics using CO2 and excimer lasers. This was done by producing 50 μm spaced (with depths between 1 and 10 μm) trench-like patterns using various laser parameters such as varying the laser power for the CO2 laser and number of pulses for the excimer laser. Topographical changes were analysed using optical microscopy and white light interferometry which indicated that both laser systems can be implemented for modifying the topography of nylon 6,6. Variations in the surface chemistry were evaluated using energy-dispersive X-ray spectroscopy and x-ray photoelectron spectroscopy analysis and showed that the O2 increased by up to 1.5% At. and decreased by up to 1.6% At. for the CO2 and F2 laser patterned samples, respectively. Modification of the wettability characteristics was quantified by measuring the advancing contact angle, which was found to increase in all instances for both laser systems. Emery paper roughened samples were also analysed in the same manner to determine that the topographical pattern played a major role in the wettability characteristics of nylon 6,6. From this, it is proposed that the increase in contact angle for the laser processed samples is due to a mixed intermediate state wetting regime owed to the periodic surface roughness brought about by the laser induced trench-like topographical patterns

Wettability and osteoblast cell response modulation through UV laser processing of nylon 6,6

With an ageing population the demand for cheap, efficient implants is ever increasing. Laser surface treatment offers a unique means of varying biomimetic properties to determine generic parameters to predict cell responses. This paper details how a KrF excimer laser can be employed for both laser-induced patterning and whole area irradiative processing to modulate the wettability characteristics and osteoblast cell response following 24 hour and 4 day incubation. Through white light interferometry (WLI) it was found that the surface roughness had considerably increased by up to 1.5 µm for the laser-induced patterned samples and remained somewhat constant at around 0.1 µm for the whole area irradiative processed samples. A sessile drop device determined that the wettability characteristics differed between the surface treatments. For the patterned samples the contact angle, θ, increased by up to 25° which can be attributed to a mixed-state wetting regime. For the whole area irradiative processed samples θ decreased owed to an increase in polar component, γP. For all samples θ was a decreasing function of the surface energy. The laser whole area irradiative processed samples gave rise to a distinct correlative trend between the cell response, θ and γP. However, no strong relationship was determined for the laser-induced patterned samples due to the mixed-state wetting regime. As a result, owed to the relationships and evidence of cell differentiation one can deduce that laser whole area irradiative processing is an attractive technology for employment within regenerative medicine to meet the demands of an ageing population

Effect of Number of Electrodes on Electrical Performance of Surface Dielectric Barrier Discharge Plasma Actuator

Dielectric barrier discharge (DBD) represent their wide application in controlling aerodynamic flow by plasma actuators. Their effectiveness is affected by the shape, type size, and thickness of electrodes. This paper investigates the influence of the number of electrodes on the electrical functioning of surface DBD plasma actuator. For this purpose, five different configurations of plasma actuator with varying electrodes number from 4 to 8 are tested. The gap between the electrodes and the length and width of each electrode remains constant in all these configurations. It was found that on increasing the number of electrodes and the applied frequency (1 kHz to 5 kHz) the value of maximum withstand voltage was decreased. However, the discharge power was increasing slightly on increasing the number of electrodes. This slight change in discharge power resulted in the significant plasma formation on the surface of the plasma actuator, the effect was visually captured by a CCD camera

Block backward differentiation formulas for solving first and second order fuzzy differential equations

In this thesis, the concerns are mainly in modifying existence method of Block Backward Differentiation Formula (BBDFs) for solving first order fuzzy differential equation, second order non-stiff and stiff fuzzy differential equations (FDEs). This method will solve the Initial Value Problems (IVPs) of FDEs using constant step size. The first part of the thesis discussed the combination of BBDF and Block Simpson into Hybrid method for solving first order FDEs. The subsequent part of the thesis focuses on the modification of BBDF into fuzzy version of BBDF for solving second order non-stiff FDEs and second orders stiff FDEs. Algorithm was developed to run the FDEs problems in Microsoft Visual C++ environment to obtain exact and approximate solutions. The algorithm of existing BBDF was modified into fuzzy version. The BBDFs method approximates the solution at two points concurrently. Therefore, numerical results show that the proposed methods reduce the execution time when compared to the Backward Differentiation Formula (BDF). In order to compute the error norm, the difference between the approximate solutions and the exact solutions was calculated. The numerical results also show the proposed method produces smaller errors when compared to modified Euler method. The accuracy of the solutions obtained by BBDF and BDF are comparable particularly when the finer step sizes are used. However, in term of execution time, the proposed method BBDF outperformed BDF method. The solutions obtained were illustrated by graphs. In conclusion, the numerical results clearly demonstrate the efficiency of using BBDF methods proposed in this study for solving fuzzy differential equations. From the results of tests problems, the modified BBDF method reveals that the execution time has been reduced and the numerical result is accurate, which proves its superiority on the existing methods

BIOMETRIC-ENHANCED CAR SECURITY SYSTEM

Face recognition has garnered much interest over the last decade, due to the need for reliable personal identification security. At the same time, the Internet of Things (IoT) have also gathered the attention of the masses from high-end technology device makers to average everyday users. These emerging trends are made possible because of the giant leaps made in hardware and software advancements over the years. Eventually, these trends made their ways to the automotive industry as the automotive makers continue to push the boundaries to satisfy customers’ needs for security and safety. Whilst automakers continuously searching for methods to improve the car security system but sometimes their efforts are not enough to keep off the unwavering motives of car thieves. On average, billions of dollars are lost to vehicle theft worldwide annually. This research project focuses on creating a more innovative approach to overcome the flaws in car security system by utilizing the facial recognition and internet of things technology available in the market. The key objective of this research project is to create a robust car security system to overcome security issues such as car theft, car robbery and unauthorized driving (illegal underaged driving). To carry out this research project, the prototype that consists of Raspberry Pi 3B is used as the main computer. This car security system will feature the facial recognition that is powered by OpenCV. Besides, the system is incorporated with a smartphone application which allows the car owner to have some control over the car while receiving alerts and information regarding the car. Pairing wireless connection and GPS enable the car system to send GPS location and other information to the car owner via the internet connection

Modified multiple generalized regression neural network models using fuzzy C-means with principal component analysis for noise prediction of offshore platform

A modified multiple generalized regression neural network (GRNN) is proposed to predict the noise level of various compartments onboard of the offshore platform. With limited samples available during the initial design stage, GRNN can cause errors when it maps the available inputs to sound pressure level for the entire offshore platform. To obtain more relevant group for GRNNs training, fuzzy C-mean (FCM) is used. However, outliers in some group may interfere the prediction accuracy. The problem of selecting suitable inputs parameters (in each cluster) is often impeded by lack of accurate information. Principal component analysis (PCA) is used to ensure high relevance input variables in each cluster. By fusing multiple GRNNs by an optimal spread parameter, the proposed modeling scheme becomes quite effective for modeling multiple frequency-dependent data set (ranging from 125 to 8000 Hz) with different input parameters. The performance of FCM-PCA-GRNNs has improved significantly as the results show a 25% improvement on the spatial sound pressure level (SPL) and 85% improvement on the spatial average SPL than just GRNNs alone. By comparing with data obtained from real engine room on a jack-up rig, the FCM-PCA-GRNNs noise model performs better with around 16% less error than the empirical-based acoustic models. Additionally, the results show comparable performance to statistical energy analysis that requires more time and resources to solve during the early stage of the offshore platform design