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- Andersson, Mathias H., 1976-
(författare)
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Offshore wind farms - ecological effects of noise and habitat alteration on fish
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- There are large gaps in our understanding how fish populations are affected by the anthropogenic noise and the alteration of habitat caused by the construction and operation of offshore wind farms. These issues are of great importance as the construction of offshore wind farms will increase all over the world in the near future. This thesis studies these effects with a focus on fish. The wind turbine foundations function as artificial reefs and are colonized by invertebrates, algae and fish. The epibenthic assemblages are influenced by factors such as hydrographical parameters, time of submergence, distance to natural hard bottom, material and texture (PAPER I, II). Once an epibenthic assemblage has been developed, fish utilize it for different ecosystem services such as food, shelter, and spawning and nursery area. Benthic and semi-pelagic species show a stronger response to the introduced foundation than pelagic species, as it is the bottom habitat that has mainly been altered (PAPER I, II). Pelagic species could be positively affected by the increased food availability - but it takes time and the effect is local. Construction noise like pile driving creates high levels of sound pressure and acoustic particle motion in the water and seabed. This noise induces behavioural reactions in cod (Gadus morhua) and sole (Solea solea). These reactions could occur up to tens of kilometres distance from the source (PAPER III). During power production, the wind turbines generate a broadband noise with a few dominating tones (PAPER IV, V), which are detectable by sound pressure sensitive fish at a distance of several kilometres even though intense shipping occurs in the area. Motion sensitive species will only detect the turbine noise at around a ten meter distance. Sound levels are only high enough to possibly cause a behavioural reaction within meters from a turbine (PAPER IV, V).
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| 2. |
- Jansson, Magnus
(författare)
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On performance analysis of subspace methods in system identification and sensor array processing
- 1995
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis addresses the issue of performance analysis of subspace-based parameter estimation methods in two different applications, namely system identification and sensor array processing. The objective is to study the quality of the estimated models as the amount of data increases, and to suggest improvements and give user guidelines. First, state-space subspace system identification (4SID) methods are formulated in a linear regression framework. This allows us to analyze the problem in a more traditional way. One advantage is that this explains more clearly the effect of the partition of the data in past and future, which is done in 4SID. Also this formulation is useful to relate and compare different proposed approaches to 4SID. The problem of estimating the poles of dynamical systems is considered. In particular, the statistical asymptotic distributions of the parameter estimates of two different 4SID pole estimation methods are studied. The first method is common in 4SID and makes use of the shift invariance structure of the observability matrix, while the second method is a recently proposed weighted least-squares method. From these results the choice of user-specified parameters is discussed, and it is shown that this choice indeed may not be obvious for the shift invariance method. A simple example is provided to illustrate the problem. However, this problem can be mitigated using more of the system structure. It is also shown that a proposed row weighting matrix in the subspace estimation step does not affect the asymptotic properties of the pole estimates. In the second part focusing on sensor array signal processing, parameter estimation from sparse linear arrays is addressed. An algorithm based on the Expectation-Maximization approach is derived. This is an iterative algorithm for solving maximum likelihood problems. In our application a powerful method for uniform linear arrays is used in the maximization step. The use of preprocessing of the covariance matrix before applying a direction of arrival estimation algorithm is also considered. In particular, linear preprocessing of covariance data in conjunction with weighted subspace fitting is analyzed and the asymptotic distribution of the parameter estimates is derived. Some possible applications when the preprocessing and the analysis may be useful are also given.
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