Description:
This work has been supported by the National Key Research and Development Program of China (2017YFB0902705), State Key Laboratory of Electrical Insulation and Power Equipment (EIPE20210), the National Natural Science Foundation of China (51907148), the Youth Fund of State Key Laboratory of Electrical Insulation and Power Equipment (EIPE19308), the Foundation Project of State Grid in Shaanxi province section, China (SGSNKY00SPJS1900302). The authors thank the Natural Science Basic Research Plan in Shaanxi Province of China (2019JQ-070).
Description:
Recently, street furniture, including bins, seats, and bus shelters, has become smart as it has been equipped with environmental sensors, wireless modules, processors, and microcontrollers. Accordingly, smart furniture is expected to become a vital part of the IoT infrastructure and one of the drivers of future smart cities. This work focuses on how smart street furniture can be exploited within the IoT architecture as a basis of recommender systems, toward achieving smart cities' different components. We present and discuss recent relevant work as well as the key challenges and opportunities for future research. We explain that much work is still required when it comes to combining scalability, real-time processing, smart furniture, and recommender systems.
Description:
The degradation of insulation systems is a complex physical process, many parameters act at the same time thus making the interpretation extremely difficult. The insulation is very much responsive in transformer serving closer to design life. Strategic maintenance and operational procedures are best formulated where the condition of existing unit has been accurately assessed. To facilitate asset management and decision making, asset's condition assessment is vital using reliable, non-intrusive diagnostics and monitoring tools together with expert system. Transformer assessment can be carried out effectively by identifying and integrating its criticalities using fuzzy logic technique. In this research, asset management and decision making model has been developed using diagnostics and data interpretation techniques based on fuzzy logic approach. Enhance reliability could be achieved by integrating real time condition monitoring, maintenance, management activities and cost effective optimization techniques. This model facilitates effectively to address criticalities and allow better planning, maintenance approach as well as to predict the remnant life of the asset within a practical accuracy.