PROTECTING RESOURCES IN THE LAKE WASHINGTON SHIP CANAL USING REAL-TIME ECOLOGICAL RISK ANALYSIS

2001 ◽  
Vol 2001 (2) ◽  
pp. 1535-1540
Author(s):  
David G. Mora
Keyword(s):  
Risk Analysis ◽  
Real Time ◽  
Geographic Area ◽  
Trade Offs ◽  
Resource Protection ◽  
Lake Washington ◽  
Response Plan ◽  
Conflicting Interests ◽  
Response Decisions ◽  
Ecological Risk Analysis

ABSTRACT Response managers and resource protection specialists often face difficult environmental trade-offs. Many decisions are time sensitive so decision-making criteria developed prior to an actual spill event will often hold sway. Despite such planning efforts all too frequently there is great room for doubt and plenty of room for speculation regarding response decisions. Response planners frequently debate environmental trade-offs associated with dispersant applications, shoreline cleanup strategy, etc. Broad assumptions and uncertainty are in large part the fuel for such debates. This paper focuses on the dilemmas associated with developing a particular response plan in a particular geographic area (i.e., for the Lake Washington Ship Canal. Development of this plan is important because of needs for rapid response decisions in an area involving many conflicting interests. This paper suggests an approach for developing this plan utilizing real time risk analysis.

Passthrough+

2020 ◽  
Vol 3 (1) ◽  
pp. 1-17
Author(s):  
Gaurav Chaurasia ◽  
Arthur Nieuwoudt ◽  
Alexandru-Eugen Ichim ◽  
Richard Szeliski ◽  
Alexander Sorkine-Hornung
Keyword(s):  
Real Time ◽  
Video Encoding ◽  
View Synthesis ◽  
Performance Constraints ◽  
Trade Offs ◽  
Stereoscopic View ◽  
Target User ◽  
Spatio Temporal ◽  
And Performance ◽  
3D Scene

We present an end-to-end system for real-time environment capture, 3D reconstruction, and stereoscopic view synthesis on a mobile VR headset. Our solution allows the user to use the cameras on their VR headset as their eyes to see and interact with the real world while still wearing their headset, a feature often referred to as Passthrough. The central challenge when building such a system is the choice and implementation of algorithms under the strict compute, power, and performance constraints imposed by the target user experience and mobile platform. A key contribution of this paper is a complete description of a corresponding system that performs temporally stable passthrough rendering at 72 Hz with only 200 mW power consumption on a mobile Snapdragon 835 platform. Our algorithmic contributions for enabling this performance include the computation of a coarse 3D scene proxy on the embedded video encoding hardware, followed by a depth densification and filtering step, and finally stereoscopic texturing and spatio-temporal up-sampling. We provide a detailed discussion and evaluation of the challenges we encountered, as well as algorithm and performance trade-offs in terms of compute and resulting passthrough quality.;AB@The described system is available to users as the Passthrough+ feature on Oculus Quest. We believe that by publishing the underlying system and methods, we provide valuable insights to the community on how to design and implement real-time environment sensing and rendering on heavily resource constrained hardware.

Crash risk analysis during fog conditions using real-time traffic data

2018 ◽  
Vol 114 ◽  
pp. 4-11 ◽  
Author(s):  
Yina Wu ◽  
Mohamed Abdel-Aty ◽  
Jaeyoung Lee
Keyword(s):  
Risk Analysis ◽  
Real Time ◽  
Crash Risk ◽  
Traffic Data ◽  
Real Time Traffic

Real‐time risk analysis using Java concepts

1998 ◽  
Vol 6 (5) ◽  
pp. 212-217
Author(s):  
L. Labuschagne ◽  
J.H.P. Eloff
Keyword(s):  
Risk Analysis ◽  
Real Time

Technical and Engineering Decisions That Drive Choices for Predictive Analytics of Plant Data

2011 ◽  
Author(s):  
William Nieman
Keyword(s):  
Real Time ◽  
Predictive Analytics ◽  
Current Data ◽  
Operating Conditions ◽  
Maintenance Cost ◽  
Real Time Systems ◽  
Efficient Technology ◽  
Time Model ◽  
Trade Offs ◽  
Time Systems

Power generation has the goal of maximizing power output while minimizing operations and maintenance cost. The challenge for plant manager is to move closer to reliability limits while being confident the risks of any decision are understood. To attain their goals and meet this challenge they are coming to realize that they must have frequent, accurate assessment of equipment operating conditions, and a path to continued innovation-. At a typical plant, making this assessment involves the collection and effective analysis of reams of complex, interrelated production system data, including demand requirements, load, ambient temperature, as well as the dependent equipment data. Wind turbine health and performance data is available from periodic and real-time systems. To obtain the timeliest understanding of equipment health for all the key resources in a large plant or fleet, engineers increasingly turn to real-time, model-based solutions. Real-time systems are capable of creating actionable intelligence from large amounts and diverse sources of current data. They can automatically detect problems and provide the basis for diagnosis and prioritization effectively for many problems, and they can make periodic inspection methods much more efficient. Technology exists to facilitate prediction of when assets will fail, allowing engineers to target maintenance costs more effectively. But, it is critical to select the best predictive analytics for your plant. How do you make that choice correctly? Real-time condition monitoring and analysis tools need to be matched to engineering process capability. Tools are employed at the plant in lean, hectic environments; others are deployed from central monitoring centers charged with concentrating scarce resources to efficiently support plants. Applications must be flexible and simple to implement and use. Choices made in selection of new tools can be very important to future success of plant operations. So, these choices require solid understanding of the problems to be solved and the advantages and trade-offs of potential solutions. This choice of the best Predictive Analytic solution will be discussed in terms of key technology elements and key engineering elements.

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