This paper presents a novel coil structure employing two parallel-connected subunit rectangular coils. The structure is based on the fact that the rectangular coils are less sensitive to misalignment along its longer side. Therefore, two identical subunit rectangular coils are vertically-oriented to one another and connected in parallel to form a cross-shape coil. The cross-shape coils have the advantage of better misalignment tolerance as compared to circular and square coils with similar footprints at the cost of an increased wire usage. Electromagnetic simulations and experiments on various small-sized coils are performed to verify the advantages of the proposed coil structure. Based on the simulation and measurement results, cross-shape coils exhibit not only better tolerance to misalignment, but also smaller self-inductance values resulting in larger coupling coefficients as compared to square and circular coils. A large cross-shape coil pair consisting of 100 × 70 cm subunit rectangles are fabricated with copper tubes and utilized in a frequency-tuned wireless power transfer system. When coils are separated by 17 cm, a near constant efficiency of more than 89% up to 15 cm misalignment along xor y-directions and 13 cm along diagonal direction is obtained in the frequency-tuned system.
Nowadays artificial intelligence techniques such as artificial neural networks, support vector machines, fuzzy logic, Markov models etc. have been started to use in smart home systems to automate actions executed by inhabitants. In order to make sure that algorithms work correctly, they need to be tested and improved. For that, we need data sets to use in testing. These datas could be generated in real life environment, as well as in virtual environment with ease. Synthetic data generation softwares are used to generate these data sets. In this paper, in order to test artificial intelligence techniques used in smart home systems, a software that generates synthetic data sets by mimicking daily human activities is developed. A family including 5 people with daily life scenarios is created to test the developed software. Subsequently according to the scenarios, a data set for a year is created by the software and tested its validaty using statistical methods. Generated data sets and obtained test results are introduced and the developed software was found to be successful.
In this study, a customized simulation has been developed to test artificial intelligence algorithms for smart homes. In the simulation, a house with the desired number of rooms can be created and smart home components with the desired number and different tasks can be added to this house. Then, virtual individuals who will use this simulated house and weekly living scenarios for these individuals can be created. After all these operations, the system simulates a smart home in real time according to the directives from outside operators. During the simulation, the desired artificial intelligence algorithms can be run on the system and their operations can be monitored. In addition, the system can be intervened from the planned scenarios to create desired conditions at any time.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.