The present work aspires to contribute to the discussion on the relationship between macroscopic measurements and microstructure, helping establish a methodology that will allow the quantitative assessment of the effect of strain on magnetic properties in the plastic deformation regime. In particular, we study the effect of strain on the magnetization process as a result of varying the anisotropy profile at the grain level. Results on micromagnetic calculations of hysteresis loops for various configurations of magnetic anisotropy are shown and discussed against the interplay between the energy terms involved in the calculations, namely anisotropy, demagnetizing, and exchange. The results are in line with previously obtained results using vector Preisach modeling with the Stoner–Wohlfarth model acting both as a switching and rotation mechanism. The hysteresis loop phenomenology is consistent with the emergence of a hard phase in the form of a boundary around soft grains which is assumed to be the result of the onset of compressive stresses in the plastic region. Future research will be oriented toward the study of the effect of the secondary peak in differential permeability, which is observed experimentally in the plastic deformation region, and its dependence on the angle of misalignment between the hard boundary and the soft grain.
Solar exposure of streets and parking spaces in dense urban areas varies significantly due to the infrastructure: buildings, parks, tunnels, multistorey car parks. This variability leaves space for both real-time and offline route and parking optimization for solar-powered vehicles. In this chapter we present Solar Car Optimized Route Estimation (SCORE), our optimization system based on historic and current solar radiance measurements. In addition to the comprehensive review of SCORE, we offer a new perspective on it by embedding it in the bigger picture of smart cities (SC): we analyze SCORE's relationship with the smart power generation and distribution systems (smart grid), novel transportation paradigms and communication advancements. While the previous work on SCORE was focused on technical challenges which are described in the first part of this chapter (optimization, communication, sensor data collection and fusion), here we proceed with a systemic approach and observe a SCORE-equipped unit in the near-future society, examine the sustainability of the model and possible business models based on it. We consider the problem of vehicle routing and congestion avoidance using incentives for users on non-critical journeys and co-existence of SCORE and non-SCORE using vehicles. Realistic pointers for SCORE-aware design of infrastructure are also given, both for improved data collection and improved solar exposure while considering trade-offs for non-SCORE users.
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.
Ova stranica koristi kolačiće da bi vam pružila najbolje iskustvo
Saznaj više