Search

Background subtraction technology is a very important part in the field of video surveillance applications. The common matrix decomposition methods based on robust principal component analysis vectorized video sequences, which destroys the spatial structure and spatio-temporal continuity of videos. Aiming at this problem, a model based on tensor robust principal component analysis was proposed. In the new model, an improved tensor nuclear norm was used to constrain the background, which strengthened the low rank of background and improved the accuracy of foreground background separation. And $$L_{1,1,2}$$ norm was applied to constrain foreground and enhanced the tube sparsity and spatio-temporal continuity of foreground, which improved the accuracy of foreground extraction. Experimental results show that the proposed algorithm outperforms multiple state-of-the-art methods both in qualitative and qualitative aspects, especially, for the videos with the multi-target or in bad weather.

The paper focuses on applying optimal control solutions to combined energy storage and attitude control system (CEACS) under different reference missions. In previous researches, the proportional-integral-derivative (PID) control method, the PID-active force control method and H2 control were tested for CEACS and achieved its mission requirement. However, problems such as the in-orbit system uncertainties affect the PID control performances. Thus, two optimal control methods, H2 and H∞ controls are proposed and tested on CEACS under different mission scenarios to improve its pitch attitude accuracy. Results show that both H2 and H∞ are able to achieve the reference mission requirement even under the influence of uncertainties (non-ideal). Moreover comparison between H2 and H∞ shows the H2 is a better control option for CEACS in terms of disturbance rejection.

The combined energy storage and attitude control system (CEACS) combines both energy storage and attitude control modules via the flywheel technology. Previously only the conventional control methods were tested for CEACS. In this paper, H2 and H-infinity control methods are implemented in CEACS. The satellite attitude control performances show that both control options can be employed for a good attitude pointing accuracy. (Abstract)

In this diploma work for Bachelor’s degree Electric Power Engineering in the first part of the work an estimate and modeling was done of the amount of available substrate for a suggested smaller bio gas plant placed in Övertorneå. Substrate was supplied from farms, solid waste from waste water plants and food waste from both Övertorneå and Ylitonio. Also included in the work was a modeling of potential biogas production and a suitable use of the biogas. In this modeling there is also an assessment if the transports of substrate are energy sustainable.The possible uses for the gas are heat production, combined heat and power production, vehicle gas production and hydrogen gas production, both for vehicles as well as combined heat and power production using fuel cells. The conclusion was that the most suitable alternative was that the biogas should be used for traditional combined heat and power production but that in the future an upgrade to combined heat and power production from hydrogen gas might be possible if the technology is deemed mature.In the second part of the work en compilation and assessment based on a literature study of the different possible techniques application for combined heat and power (CHP) with their inherent advantages and disadvantages of those techniques, with the purpose to be able to choose later the best power generator for the plant in this project. Validerat; 20150607 (global_studentproject_submitter)

Dimirovski, Georgi M. (Dogus Author) -- Conference full title: 2008 American Control Conference : Seattle, WA, USA, July 11-13, 2008 The two-stage Unscented Kalman Filter (TUKF) is proposed to consider the nonlinear system in the presence of unknown random bias in a number of practical situations. The adaptive fading UKF is designed by using the forgetting factor to compensate the effects of incomplete information. The TUKF to estimate unknown random bias is designed by using the adaptive fading UKF. This filter can be used for nonlinear systems with unknown random bias on the assumption that the stochastic information of a random bias is incomplete. The stability of the TUKF is analyzed and ensured under certain conditions. The performance of the TUKF is verified by using MATLAB simulation on the high-update rate Wheel Mobile Robot (WMR).

In this paper we introduce a decomposition theory for fuzzy cognitive maps (FCMs). First, we partition the set of vertices of an FCM into blocks according to an equivalence relation on the set, and by regarding these blocks as vertices we construct a quotient FCM. Second, each block induces a natural sectional FCM of the original FCM. We apply our theory to a practical university management example. The framework proposed is effective for calculating causal inferencing in real-world applications.