Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shape

The assignment of individual fish to its stock of origin is important for reliable stock assessment and fisheries management. Otolith shape is commonly used as the marker of distinct stocks in discrimination studies. Our literature review showed that the application and comparison of alternative statistical classifiers to discriminate fish stocks based on otolith shape is limited. Therefore, we compared the performance of two traditional and four machine learning classifiers based on Fourier analysis of otolith shape using selected stocks of Atlantic cod (Gadus morhua) in the southern Baltic and Atlantic herring (Clupea harengus) in the western Norwegian Sea, Skagerrak and the southern Baltic Sea. Our results showed that the stocks can be successfully discriminated based on their otolith shapes. We observed significant differences in the accuracy obtained by the tested classifiers. For both species, support vector machines (SVM) resulted in the highest classification accuracy. These findings suggest that modern machine learning algorithms, like SVM, can help to improve the accuracy of fish stock discrimination systems based on the otolith shape.Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shapesubmittedVersio

Mechatronic Coupling System for Cooperative Manufacturing with Industrial Robots

Rising product variants and shortened product life cycles require more flexible and universally utilizable production systems and machines. Consequently, it can be expected that the importance of industrial robots in production will continuously increase, due to their suitability to take over the role of a universal production machine. However, robots are not yet able to fulfill this role. Industrial use of robots has so far been limited mainly to simple transport and handling tasks in the context of human-robot collaboration as well as highly repetitive automated tasks in the context of manufacturing and assembly. For universal use, robots must be capable to perform more demanding tasks in manufacturing with higher requirements on mechanical stiffness and accuracy. Therefore, this paper presents a mechatronic system to couple two robots to a parallel kinematic system to temporarily increase the mechanical stiffness. The coupled state of the robots allows load sharing, higher process forces and eventually higher precision. The overall goal is to enable robots to perform more demanding manufacturing tasks and thus to be utilized in a wider range of applications. Design requirements, the development approach and optimization methods of the first coupling module prototype will be presented and discussed. The next development steps, a future demonstration system and possible use cases for the coupling module will be shown in the outlook