Improving Face Pose Estimation Using Long-Term Temporal Averaging for Stochastic Optimization

Author(s):  
Nikolaos Passalis ◽  
Anastasios Tefas
Author(s):  
Congjian Wang ◽  
Diego Mandelli ◽  
Shawn St Germain ◽  
Curtis Smith ◽  
David Morton ◽  
...  

Abstract As commercial nuclear power plants (NPPs) pursue extended plant operations in the form of Second License Renewals (SLRs), opportunities exist for these plants to provide capital investments to ensure long-term, safe, and economic performance. Several utilities have already announced their intention to pursue extended operations for one or more of their NPPs via SLR2. The goal of this research is to develop a risk-informed approach to evaluate and prioritize plant capital investments made in preparation for, and during the period of, extended plant operations to support decisions in NPP operations. In order to prioritize project selection via a risk-informed approach we developed a single decision-making tool that integrates safety/reliability, cost, and stochastic optimization models to provide users with data analysis capabilities to more cost effectively manage plant assets. Both stochastic analysis methods — such as Monte Carlo-based sampling strategies — and multi-stage stochastic optimization strategies are employed to provide priority lists to decision-makers in support of risk-informed decisions. We applied the proposed method to a trial application of projected replacement/refurbishment expenditures for plant capital assets (i.e., structures, systems, and components [SSCs]). The objective is to optimize the SSC replacement/refurbishment schedule in terms of economic constraints, data uncertainties, and SSC reliability data, as well to generate a priority list for maximizing returns on investment.


Energy ◽  
2019 ◽  
Vol 175 ◽  
pp. 781-797 ◽  
Author(s):  
Viviani C. Onishi ◽  
Carlos H. Antunes ◽  
Eric S. Fraga ◽  
Heriberto Cabezas

Author(s):  
Hesham Ismail ◽  
Balakumar Balachandran

In carrying out simultaneous localization and mapping, a mobile vehicle is used to simultaneously estimate its position and build a map of the environment. The long-term goal of this work is to build an autonomous inspection mobile vehicle for oil storage tanks and pipelines. The harsh environmental conditions in storage tanks and pipelines limit the types of feature extraction sensors and vehicle pose estimation sensors that one can use. Here, a SOund Navigation And Ranging (SONAR) sensor will be used for feature extraction, and a gyroscope and an encoder will be used for vehicle pose estimation. The integration of these sensors (SONAR, encoder, and gyroscope) will be discussed in this paper, along with the use of a recently developed algorithm fusion for SONAR sensors. The integration of the sensors represents a step towards implementation of concurrent localization and mapping progress in harsh environments.


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