— This paper presents a study of the effect of microstructure and magnetic texture on the hysteresis loop through quasi-static micromagnetic simulations using the open-source software OOMMF. Results show that microstructure and magnetic texture parameters can be used to control the coercivity. The anisotropy constant is the parameter mainly controlling the coercivity. The increase in the volume fraction of hard inclusions in a soft matrix typically leads to higher coercivity. In the case of randomly oriented inclusions, the calculated coercivity is lower than that for the homogeneous soft case which is explained through the prominent anisotropy energy density compared to the much weaker exchange energy density. The results will be used to correlate simulation parameters with magnetic parameters obtained from major hysteresis loop measurements.

Noninvasive load monitoring have been investigated by researchers for decades due to its cost-effective benefits. Upon introduction of smart meters, obtaining data about power consumption of households became easier. Numerous different techniques have been applied on the power consumption data to gain useful information out of it. This study applies machine learning techniques (Bayes network, random forest and rotational forest) to determine the operation state of households, where households are assumed to be either in ON or OFF state. Tracebase power consumption signature repository was used to train and test proposed machine learning models. Tracebase dataset was preprocessed to generate 4 different datasets. Test results have shown that these machine learning algorithms are able to estimate operation state with high accuracy and Bayes network shows outstanding performance among them with overall accuracy of 95%. Proposed method is extremely cost-effective for load monitoring and could replace some of the physical sensors in the smart houses.

Solar Car Optimized Route Estimation (SCORE) has been proposed in an earlier publication as an alternative navigation principle for solar cars, conducting route optimization based on both distance and solar irradiance data. This paper gives details about the implementation and discusses results of SCORE's use, suggesting possible limitations and future research directions. The results show limited applicability of solar irradiance data for route optimization, but suggesting that parking place selection is an important aspect that needs to be taken care of. The implementation uses both a MATLAB testbed application and C/C++ code for TI's ARM Cortex-M4F based TM4C123G LaunchPad, and the final version of the SCORE client is placed in a custom built solar vehicle. Combined with the previously developed server for sensor data collection and data processing and sensor transmitter infrastructure for solar irradiation, the route optimization system is fully operational.