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It is anticipated that passengers in autonomous vehicles will be more occupied with in-vehicle activities. Loss of the authority on driving and engaging in non-driving tasks could cause lower predictability of car motions. This decrease in predictability is expected to increase the sensitivity to carsickness. It appears that it is crucial to develop controllers for autonomous driving with the capability of improving passenger comfort by reducing carsickness. In this regard, it can be asked how the motion variables can be used for the minimization of a carsickness-related measure, while the vehicle is required to follow a given path. In this study, an optimal control approach is being proposed to minimize a quantitative measure of carsickness. In order to address carsickness during autonomous maneuvers, the well-known motion sickness dose value formulation in ISO 2631-1 is augmented with horizontal direction motion components to define a performance measure. The performance measure includes the motion sensed in vestibular system rather than the motion occurring in the vehicle itself. Therefore, mathematical model of the vestibular system is included in the design of controller. Effects of acceleration and jerk are included in performance measure simultaneously. Control oriented linear parameter varying vehicle model is developed to design the path following controller. By means of simulation studies in which path following control is implemented, motion sickness dose values of the controlled vehicle are examined. It is shown by a regular lane change test at various speeds that the proposed controller, which seeks the minimization of the motion sickness dose value, achieves a reduction of the acceleration and jerk felt by a passenger, while the vehicle follows the given path.

Along with technological developments and increasing population, people are in need of more energy sources. This need has led researchers to go towards new energy generation methods. One of these methods is hydrokinetic energy generation, which has been studied intensively in recent years. In this study, complete design of a hydrokinetic turbine that converts kinetic energy into mechanical and electrical energy with the most efficiency using tidal water is proposed. Moreover, an undershot water wheel system is designed to gain the least dissipationless conversion of kinetic energy. The design of the hydrokinetic energy generation system is developed considering the environmental and maintenance factors, maximum efficiency and buoyancy. Calculation for the velocity of the turbine is made by using Betz's law, usually used for wind energy conversion systems. Conversion of obtained mechanical energy from the turbine to electrical energy is supplied by using a proper alternator system. UDC Classification: 621.22 DOI: http://dx

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Abstract:In this study, an artificial neural network was developed for estimating Hashimoto's Thyroiditis subgroups. Medical analysis and measurements from 75 patients were used to determine the parameters most effective on disease sub-groups. The study used statistical analyses and an artificial neural network that was trained by the determined parameters. The neural network had four inputs: thyroid stimulating hormone, free thyroxine (fT4), right lobe size (RLS), and RLS 2 -fT4 4 , and two outputs for three groups: euthyroid, subclinical, and clinical. After training, the network was tested with data collected from 30 patients. Results show that, overall, the neural network estimated the sub-groups with 90% accuracy. Hence, the study showed that determination of Hashimoto's Thyroiditis sub-groups can be made via designed artificial neural network.UDC Classification: 004.8, DOI: http://dx