3 resultados para Large detector-systems performance

em QSpace: Queen's University - Canada


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Recreational fisheries in North America are valued between $47.3 billion and $56.8 billion. Fisheries managers must make strategic decisions based on sound science and knowledge of population ecology, to effectively conserve populations. Competitive fishing, in the form of tournaments, has become an important part of recreational fisheries, and is common on large waterbodies including the Great Lakes. Black Bass, Micropterus spp., are top predators and among the most sought after species in competitive catch-and-release tournaments. This study investigated catch-and-release tournaments as an assessment tool through mark-recapture for Largemouth Bass (>305mm) populations in the Tri Lakes, and Bay of Quinte, part of the eastern basin of Lake Ontario. The population in the Tri Lakes (1999-2002) was estimated to be stable between 21,928-29,780, and the population in the Bay of Quinte (2012-2015) was estimated to be between 31,825-54,029 fish. Survival in the Tri Lakes varied throughout the study period, from 31%-54%; while survival in the Bay of Quinte remained stable at 63%. Differences in survival may be due to differences in fishing pressure, as 34-46% of the Largemouth Bass population on the Tri Lakes is harvested annually and only 19% of catch was attributed to tournament angling. Many biological issues still surround catch-and-release tournaments, particularly concerning displacement from initial capture sites. In the past, the majority of studies have focused on small inland lakes and coastal areas, displacing bass relatively short distances. My study displaced Largemouth and Smallmouth Bass up to 100km, and found very low rates of return; only 1 of 18 Largemouth Bass returned 15 km and 1 of 18 Smallmouth Bass returned 135 km. Both species remained near the release sites for an average of approximately 2 weeks prior to dispersing. Tournament organizers should consider the use of satellite release locations to facilitate dispersal and prevent stockpiling at the release site. Catch-and-release tournaments proved to be a valuable tool in assessing population variables and the effects of long distance displacement through the use of mark recapture and acoustic telemetry on large lake systems.

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The real-time optimization of large-scale systems is a difficult problem due to the need for complex models involving uncertain parameters and the high computational cost of solving such problems by a decentralized approach. Extremum-seeking control (ESC) is a model-free real-time optimization technique which can estimate unknown parameters and can optimize nonlinear time-varying systems using only a measurement of the cost function to be minimized. In this thesis, we develop a distributed version of extremum-seeking control which allows large-scale systems to be optimized without models and with minimal computing power. First, we develop a continuous-time distributed extremum-seeking controller. It has three main components: consensus, parameter estimation, and optimization. The consensus provides each local controller with an estimate of the cost to be minimized, allowing them to coordinate their actions. Using this cost estimate, parameters for a local input-output model are estimated, and the cost is minimized by following a gradient descent based on the estimate of the gradient. Next, a similar distributed extremum-seeking controller is developed in discrete-time. Finally, we consider an interesting application of distributed ESC: formation control of high-altitude balloons for high-speed wireless internet. These balloons must be steered into a favourable formation where they are spread out over the Earth and provide coverage to the entire planet. Distributed ESC is applied to this problem, and is shown to be effective for a system of 1200 ballons subjected to realistic wind currents. The approach does not require a wind model and uses a cost function based on a Voronoi partition of the sphere. Distributed ESC is able to steer balloons from a few initial launch sites into a formation which provides coverage to the entire Earth and can maintain a similar formation as the balloons move with the wind around the Earth.

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Over the past few years, logging has evolved from from simple printf statements to more complex and widely used logging libraries. Today logging information is used to support various development activities such as fixing bugs, analyzing the results of load tests, monitoring performance and transferring knowledge. Recent research has examined how to improve logging practices by informing developers what to log and where to log. Furthermore, the strong dependence on logging has led to the development of logging libraries that have reduced the intricacies of logging, which has resulted in an abundance of log information. Two recent challenges have emerged as modern software systems start to treat logging as a core aspect of their software. In particular, 1) infrastructural challenges have emerged due to the plethora of logging libraries available today and 2) processing challenges have emerged due to the large number of log processing tools that ingest logs and produce useful information from them. In this thesis, we explore these two challenges. We first explore the infrastructural challenges that arise due to the plethora of logging libraries available today. As systems evolve, their logging infrastructure has to evolve (commonly this is done by migrating to new logging libraries). We explore logging library migrations within Apache Software Foundation (ASF) projects. We i find that close to 14% of the pro jects within the ASF migrate their logging libraries at least once. For processing challenges, we explore the different factors which can affect the likelihood of a logging statement changing in the future in four open source systems namely ActiveMQ, Camel, Cloudstack and Liferay. Such changes are likely to negatively impact the log processing tools that must be updated to accommodate such changes. We find that 20%-45% of the logging statements within the four systems are changed at least once. We construct random forest classifiers and Cox models to determine the likelihood of both just-introduced and long-lived logging statements changing in the future. We find that file ownership, developer experience, log density and SLOC are important factors in determining the stability of logging statements.