Drawing and writing on the screen

This chapter focuses on the physicality of the iPad as an object, and how that physicality affects the interactions children have with the device generally, and the apps specifically. Thinking about the physicality of the iPad is important because the materials, size, weight and appearance make the iPad quite unlike most other toys and equipment in the kindergarten space. Most strikingly, this physicality does not ‘represent’ the virtual vast dimensions of the iPad brought about through the diverse functions and contents of the apps contained in it. While the iPad is small enough and functional enough to be easily handled and operated even by young children, it is capable of performing highly complex, highly technological tasks that take it beyond its diminutive dimensions. This virtual-actual contrast is interesting to consider in relation to the other resources more commonly found in a kindergarten space. While objects such as toys, bricks, building materials often do prompt the child to imagine and invent beyond the physical boundaries of the toy, they not have the same types of virtual-actual contrasts of a digital device such as the iPad. How then, might children be drawn to the iPad because of its physical, technological and virtual difference? Particularly, how might this virtual-actual difference impact on the physical skills associated with writing and drawing: skills usually learnt through the use of a pencil and paper? While the research project did not set out to compare how digital and paper-based resources affect writing and drawing skills there was great interest to see how young children negotiated drawing and writing on the shiny glass surface of the iPad.

A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.