VIRTUAL LEARNING IS NOT FOR MY CHILD! A PARENTAL PERSPECTIVE OF PRACTICES USED WITH CHILDREN WITH AUTISM DURING THE PANDEMIC

Once the COVID-19 was declared as a pandemic, most countries temporarily closed schools and shifted to home-based distance education. Each country had its own way of implementing education remotely. Turkey used an enhanced version of the currently existing Education Information Network (EBA) to deliver distance education to all children including those with autism. The rapid shift from face-to-face education to home-based virtual learning created unprecedented challenges and impacted development and learning of children with autism who often need individualized and systematic instruction. Therefore, the purpose of this study was to explore parental perceptions about effectiveness of distance education practices for children with autism and challenges they faced during visual learning. A total of 208 parents of children with autism participated in quantitative data collection while 18 also attended to individually conducted interviews. Results indicated many families did not use EBA to support their children’s learning and the content of virtual learning opportunities through EBA was not appropriate for the characteristics of children with autism. Implications for future practice and research as well as the limitations of this study were discussed

STUDYING ON THE EFFECTS OF QUENCHING RATE ON RESIDUAL STRESS IN Al-5Mg and Al-Mg-Cu ALLOYS

In aluminium alloys, Cu and Mg are added in order to increase the strength. Obtained alloys are subjected to solution treated and precipitation hardening (T6) processing. Phases are formed with simultaneous diffusions of Cu and Mg in these alloys. The purpose of this study is the modelling of elements, which are in Al-Cu, Al-Mg and Al-Cu-Mg alloys, solution treatment times into alloys both one by one and in an interrelated way and their microstructural changings. Alloys are produced by pouring to both sand and metal mould. Then, these samples are subjected to solution treated and precipitation hardening processing in different temperatures and for different periods of time. Solution treated speeds of obtained samples are examined with microstructure and image analyses through metallographic examination and a model is designed. Microhardness analyses are also made. On the other hand, residual stress of alloys are examined with hardness, DSC and microstructure analyses

İnsansız Hava ve Kara Araçları ile Gezinme

Hızla gelişen ve ucuzlayan teknoloji ile insansız hava ve yer robotları, artık sivil ve askeri amaçlar için çok yaygın kullanılmaya başlamıştır. Birbirini hareket, görev süresi, taşıma kapasitesi, görüş alanı gibi pek çok yönden tamamlayan bu iki robot türünün, farklı görevler için ortak ve birbirini tamamlar nitelikte kullanımı ise artık kaçınılmaz olmuştur.Bu projede, açık alanlarda insansız bir kara aracının (İKA) (i) belirli bir noktadan bir hedefe gitme ve (ii) belirli bir bölgede hedef bir nesneyi bulma görevlerinde, insansız bir hava aracı (İHA) ile otonom, beraber ve birbirinin yeteneklerini tamamlar nitelikte çalışması problemi üzerine odaklanılacaktır

Reinforcement Learning versus Conventional Control for Controlling a Planar Bi-rotor Platform with Tail Appendage

© 2021, The Author(s), under exclusive licence to Springer Nature B.V.In this paper, we study the conventional and learning-based control approaches for multi-rotor platforms, with and without the presence of an actuated “tail” appendage. A comprehensive experimental comparison between the proven control-theoretic approaches and more recent learning-based ones is one of the contributions. Furthermore, an actuated tail appendage is considered as a deviation from the typical multi-rotor morphology, complicating the control problem but promising some useful applications. Our study also explores, as another contribution, the impact of such an actuated tail on the overall position control for both the conventional as well as learning-based controllers. For the conventional control part, we used a multi-loop architecture where the inner loop regulates the attitude while the outer loop controls the position of the platform. For the learning controller, a multi-layer neural network architecture is used to learn a nonlinear state-feedback controller. To improve the learning and generalization performance of this controller, we adopted a curricular learning approach which gradually increases the difficulty of training samples. For the experiments, a planar bi-rotor platform is modeled in a 2D simulation environment. The planar model avoids mathematical complications while preserving the main attributes of the problem making the results more useful. We observe that both types of controllers achieve reasonable control performance and can solve the position control task. However, neither one shows a clear advantage over the other. The learning-based controller is not intuitive and the system suffers from long training times. The architecture of the multi-loop controller is handcrafted (not required for the learning-based controller) but provides a guaranteed stable behavior