Refine
Year of publication
Document Type
- Article (98)
- Conference Proceeding (8)
- Part of a Book (1)
Keywords
- MINLP (3)
- Experimental validation (2)
- Process engineering (2)
- Cooling system (1)
- Engineering optimisation (1)
- Engineering optimization (1)
- Industrial optimisation (1)
- MILP (1)
- Mixed-integer nonlinear problem (1)
- Mixed-integer nonlinear programming (1)
- Mixed-integer programming (1)
- Multi-criteria optimization (1)
- Network design (1)
- Optimization (1)
- Paper recycling (1)
- Pumping systems (1)
- Resilience (1)
- Technical Operations Research (1)
- Validation (1)
- Water distribution system (1)
Institute
- Fachbereich Medizintechnik und Technomathematik (95)
- Fachbereich Elektrotechnik und Informationstechnik (6)
- Fachbereich Bauingenieurwesen (2)
- Fachbereich Energietechnik (2)
- Fachbereich Chemie und Biotechnologie (1)
- Fachbereich Maschinenbau und Mechatronik (1)
- INB - Institut für Nano- und Biotechnologien (1)
- IfB - Institut für Bioengineering (1)
High spin states in ¹⁸⁸ Au
(1982)
A network of brain areas is expected to be involved in supporting the motion aftereffect. The most active components of this network were determined by means of an fMRI study of nine subjects exposed to a visual stimulus of moving bars producing the effect. Across the subjects, common areas were identified during various stages of the effect, as well as networks of areas specific to a single stage. In addition to the well-known motion-sensitive area MT the prefrontal brain areas BA44 and 47 and the cingulate gyrus, as well as posterior sites such as BA37 and BA40, were important components during the period of the motion aftereffect experience. They appear to be involved in control circuitry for selecting which of a number of processing styles is appropriate. The experimental fMRI results of the activation levels and their time courses for the various areas are explored. Correlation analysis shows that there are effectively two separate and weakly coupled networks involved in the total process. Implications of the results for awareness of the effect itself are briefly considered in the final discussion.
As part of a novel approach to automatic sewer inspection, this paper presents a robust algorithm for automatic flow line detection. A large image repository is obtained from about 50,000 m sewers to represent the high variability of real world sewer systems. Automatic image processing combines Canny edge detection, Hough transform for straight lines and cost minimization using Dijkstra's shortest path algorithm. Assuming that flow lines are mostly smoothly connected horizontal structures, piecewise flow line delineation is reduced to a process of selecting adjacent line candidates. Costs are derived from the gap between adjacent candidates and their reliability. A single parameter α enables simple control of the algorithm. The detected flow line may precisely follow the segmented edges (α = 0.0) or minimize gaps at joints (α = 1.0). Both, manual and ground truth-based analysis indicate that α = 0.8 is optimal and independent of the sewer's material. The algorithm forms an essential step to further automation of sewer inspection.