Compared with traditional lighting, LED has many unparalleled advantages, and is considered to be the most promising green light which may be able to substitute incandescent and fluorescent lamps. The requirement of LED's luminous efficiency grows with its increasingly application in the lighting field. This paper summarizes the development of gaining approaches of Power-type WLED and their respective advantages and disadvantages; analyses several effective programs which can improve luminous efficiency, including changing substrate materials as to enhance the heat-release performance as well as the impact of chip structure design, the packaging materials and technology or other factors; proposes a new LED packaging material- inorganic fluorescent glass, which is expected to simplify the packaging process greatly, at last, there is an expectation of High-power WLED in future application.
<p><strong>Abstract.</strong> We present a novel method to infer CO<sub>2</sub> emissions from individual power plants based on satellite observations of co-emitted nitrogen dioxide (NO<sub>2</sub>) and demonstrate its utility on US power plants, where accurate stack emission estimates of both gases are available for comparison. In the first step of our methodology, we infer nitrogen oxides (NO<sub><i>x</i></sub>) emissions from isolated power plants using Ozone Monitoring Instrument (OMI) NO<sub>2</sub> tropospheric vertical column densities (VCDs) averaged over the ozone season (May&#8211;September) and a &quot;top-down&quot; approach that we previously developed. Second, we determine the relationship between NO<sub><i>x</i></sub> and CO<sub>2</sub> emissions based on the direct stack emissions measurements reported by continuous emissions monitoring system (CEMS) programs, accounting for coal type, boiler firing type, NO<sub><i>x</i></sub> emission control device type, and changes in operating conditions. Third, we estimate CO<sub>2</sub> emissions of the ozone season for a plant using the OMI-estimated NO<sub><i>x</i></sub> emissions and the CEMS NO<sub><i>x</i></sub>&#8201;/&#8201;CO<sub>2</sub> emission ratio. We find that the CO<sub>2</sub> emissions estimated by our satellite-based method during 2005&#8211;2017 are in reasonable agreement with the CEMS measurements, with a relative difference of 8&#8201;%&#8201;&#177;&#8201;41&#8201;% (mean&#8201;&#177;&#8201;standard deviation) for the selected US power plants in our analysis. Total uncertainty in the inferred CO<sub>2</sub> estimates is partly associated with the uncertainty associated with the OMI NO<sub>2</sub> VCD data, so we expect that it will decrease when our method is applied to OMI-like sensors with improved capabilities, such as TROPOspheric Monitoring Instrument (TROPOMI) and geostationary Tropospheric Emissions: Monitoring Pollution (TEMPO). The broader implication of our methodology is that it has the potential to provide an additional constraint on CO<sub>2</sub> emissions from power plants in regions of the world without reliable emissions accounting. We explore the feasibility by applying our methodology to a power plant in South Africa, where the satellite-based emission estimates show reasonable consistency with other estimates.</p>
Efficient and accurate state detection of transmission cables is an important means to ensure reliable transmission. Aiming to realize fast and efficient transmission cable state analysis with the help of a binocular vision tool on a loop dismantling robot, this paper proposes a transmission cable state recognition method combining motion control and image segmentation technology. In this method, the fuzzy P I D control method is adopted to ensure that the wire removal robot can realize high-precision and rapid response control and effectively improve the collection quality of the cable image sample set. Meanwhile, aiming to achieve faster and more efficient data acquisition and state analysis, the state analysis model is sunk to the edge side, and the cable state detection and recognition model is constructed based on the fast RCNN model at the edge of the network to realize the in-depth extraction of feature information, enhance the transmission cable state recognition effect of the state detection model, and improve the response analysis speed of the model. The simulation results show that the accuracy of the proposed method is 97.54%, and its calculation time is 1.034 s, which can effectively realize the analysis and research of transmission cable state under complex working conditions.
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.
hi@scite.ai
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.