One of the main challenges in the design of passive suspension systems is the optimum selection of suspension system parameters. In this paper, a-four-degree-of-freedom quarter car model is implemented in order to design an optimal suspension system for better ride comfort and road holding characteristics. The mathematical model was generated in MATLAB Simulink environment for simulation. The Multi-objective particle swarm optimisation algorithm is used to optimise the suspension parameters such as suspension spring stiffness, damping coefficient of dampers, driver seat stiffness and driver seat damping coefficient. In addition, an artificial neural network model is trained to predict the root mean square values of ride comfort and road holding characteristics for a given set of input parameters by using the neural network toolbox in MATLAB. The results show that the acceleration of sprung mass and head decayed to a minimum under 2 seconds and the magnitude of the acceleration of the head was lower than that of the sprung mass. The unsprung mass was not displaced from the ground for more than 0.014m and road holding characteristics were also similar.
The static flow resistivity is a fundamental parameter for measuring and classifying the sound absorption behavior of various types of materials. Several methods have been developed for measuring the static flow resistivity acoustically. Most of these methods cannot be implemented directly in the standard tubes which are widely used for measurements of sound absorption coefficients and impedance as defined in ISO 10534.2. The accuracy of the proposed method and the tube is verified through finite element analysis and the feasibility to determine the static flow resistivity is validated through experiments. It is validated that the accuracy of the proposed method is highly dependent on the position of the acoustic center of the measurement microphones and the accuracy can be enhanced by increasing the back cavity depth and/or decreasing the measurement frequency.
In recent years, the integration of various electronic components and sensors with textiles aimed at giving additional functions has become more common. In this respect the wrist band can be made functional while retaining the aesthetic appeal and style at lower cost which is in high demand. Smart textiles are fabrics that have been designed and manufactured to include technologies that provide the wearer with increased functionality. Smart textiles can be produced by knitting, weaving and embroidering with conductive threads, conductive metal coating and screen printing that can be used to develop wearable electronic textiles but amongst these, the use of conductive inks onto textiles has gained interest due to the ease of their use and manufacturing scalability. The emergence of wireless technologies and advancement in on-body sensor design can enable change in the conventional healthcare system, replacing it with wearable ones, centered on the individual. Wearable monitoring systems can provide continuous physiological data, as well as better information regarding the general health of individuals. Thus, such vital-sign monitoring systems will reduce healthcare costs by disease prevention and enhance the quality of life. This dissertation is aimed at developing smart band by incorporating vital-sign monitoring systems. Using this assembly, the recent progress in non-invasive monitoring technologies for chronic disease management is reviewed. Devices and techniques for monitoring pulse rate and body temperatures are discussed in particular. For our research conductive ink and conductive fabrics are presented additionally. The main aim of this project is to produce a wearable wrist band which detects vital body parameters like pulse rate and temperature using sensors, conductive ink and conductive fabric. Finally, the recorded temperature and pulse rate readings are sent to mobile app via Wi-Fi
Purpose: To schedule chemotherapy drug delivery using Deterministic Oscillatory Search algorithm, keeping the toxicity level within permissible limits and reducing the number of tumor cells within a predefined time period. Methods: A novel metaheuristic algorithm, deterministic oscillatory search, has been used to optimize the Gompertzian model of the drug regimen problem. The model is tested with fixed (fixed interval variable dose, FIVD) and variable (variable interval variable dose, VIVD) interval schemes and the dosage presented for 52 weeks. In the fixed interval, the treatment plan is fixed in such a way that doses are given on the first two days of every seven weeks such as day 7, day 14, etc. Results: On comparing the two schemes, FIVD provided a higher reduction in the number of tumor cells by 98 % compared to 87 % by VIVD after the treatment period. Also, a significant reduction in the number was obtained half way through the regimen. The dose level and toxicity are also reduced in the FIVD scheme. The value of drug concentration is more in FIVD scheme (50) compared to VIVD (41); however, it is well within the acceptable limits of concentration. The results proved the effectiveness of the proposed technique in terms of reduced drug concentration, toxicity, tumor size and drug level within a predetermined time period. Conclusion: Artificial intelligent techniques can be used as a tool to aid oncologists in the effective treatment of cancer through chemotherapy.
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