Analysis of vertical light tubes, and diffuser as daylighting system in buildings is presented. The solar light energy is directly addressed to the chosen area of the building with a light tube and diffuser guiding structure. This work highlights illuminance obtained by light tubes in a three storied office building. Field investigation with a scaled prototype and simulations with HOLIGILM, and DIALux are analyzed. It is observed that light tubes can offer large savings in electricity usage by using natural daylight.
The basic need of human is increasing as they interact with different devices and also, they provide many feedbacks. Many smart devices generate high data and that can be retrieved and reviewed by humans. Applications are not fixed as it increases day to day life. Based on these data generated by different smart devices and smart city applications machine learning approach is the best adaptive solution. Rapid development in software, hardware with high speed internet connection provides large data to this physical world. The key contribution of this paper is a machine learning application survey towards smart city.
Introduces a conditional repetitive group sampling plan. The OC and ASN functions are derived by GERT approach. The present development would be a valuable addition to the literature and a useful device to quality control practitioners and also to quality control engineers and plan designers in the development of further new plans.
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