The thermal aware floor planning for VLSIs and thermal placement optimization of electronic components on printed circuit boards (PCBs) using genetic algorithms (GAs) are well studied. However, there are no technical paper on optimization of component placement considering the heat of smartglasses. In this paper, we propose a method for optimizing the placement of electronic components equipped on smartglasses using the elitist non-dominated sorting genetic algorithm (NSGA-II) and a thermal resistance circuit. Electronic components that have various dimensions and power consumptions are relocated to minimize the maximum temperature of parts around ears and areas often held by hands simultaneously. The experimental results show that the proposed method effectively reduced the maximum temperatures.
Wireless power transfer technology is installed in various devices such as smartphones and smart watches. The misalignment of devices and the increase in the number of dedicated chargers pose problems in terms of convenience and the room landscape. In this letter, we propose a parabolic spiral coil transmitter with a uniform magnetic field that is robust against misalignment and can charge multiple devices at the same time with one transmitter coil. The verification results show that the coefficients of variations of the magnetic flux density of the conventional transmitter and the proposed transmitter are 0.380 and 0.245, respectively, indicating that the proposed transmitter has a small magnetic field fluctuation.
This letter proposes a new method for obtaining self and mutual inductances in wireless power transfer (WPT) systems using a Bayesian neural network (BNN). Generally, inductance calculations using a field solver take a huge amount of time. Moreover, due to the complexity of WPT systems, there is no approximate equation for calculating inductances including ferrite shields. In this letter, nine structural parameters of a WPT system are experimentally used as inputs. The experimental results demonstrate that inductances obtained by the proposed method are within 5.1% in the maximum errors and within 1.1% in the mean absolute errors. The proposed method is about 748k times faster than the field solver in the CPU time required to obtain the inductances of one structure.
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