Energy consumption reduction has been an increasing trend in machine learning over the past few years due to its socio-ecological importance. In new challenging areas such as edge computing, energy and predictive accuracy are key variables during algorithm design implementation. State-of-the-art ensemble stream mining algorithms able create highly accurate predictions at a substantial cost. Thi...

Nowadays, numerical methods constitute an important tool in the analysis of information that cannot be obtained experimentally, or can only at a high cost subject to significant disadvantages [...]

The advent of 3D printers has been embraced globally within few years of its emergence. The surge in the acceptability of rapid manufacturing RM technology can be attributed to the depletion and cost of natural resources, waste reduction and sustainability criterion of manufactured parts. This rapidly evolving 3D printing technologies is predicted to grow exponentially especially for the manufa...

Object diagrams are generalizations of block diagrams and of bond graphs, and allow mode-ling of input/output signal ow as well as energy ow. It is shown how object diagrams can be used for the modeling of mechatronic systems, consisting of 3-dimensional mechanical systems, electrical circuits, drive trains, control blocks etc. Emphasis is given to the description of energy ow between diierent ...

• Traditional approaches, including Box–Jenkins autoregressive integrated moving average (ARIMA) model, autoregressive and moving average with exogenous variables (ARMAX) model, seasonal autoregressive integrated moving average (SARIMA) model, exponential smoothing models [including Holt–Winters model (HW) and seasonal Holt and Winters’ linear exponential smoothing (SHW)], state space/Kalman fi...