Small farmers in the Romanian dairy market: Do they have a future?

This paper investigates various modes of vertical coordination, with the focus on small farm integration in the Romanian dairy chain. It draws on results from a World Bank study based on semi-structured interviews conducted in spring 2009. The findings indicate that large and prosperous dairy chains fortify their chain efficiency by partner selection and provision of sophisticated assistance to relatively larger farmers. On the contrary, many barriers exist for small and medium-sized dairy chains (processors and farmers). The main factors hampering their potential exploitation are restricted access to inputs markets (capital, know-how) as well as poor quality of input service (agricultural service delivery, veterinary issues). The majority of cow's milk in Romania is still delivered by small farmers who have difficulties fulfilling the requirements of the modern procurement systems. However, small farmers are a relatively heterogeneous group. Hence, different development paths can be expected. In addition to working with retail chains via strengthening horizontal integration, another opportunity for small dairy farmers is to occupy a market niche. Nevertheless, some small farmers will have to leave the dairy market.vertical coordination, small farms, Romania, dairy., Livestock Production/Industries,

Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop

The process of developing control functions for embedded systems is resource-, time-, and data-intensive, often resulting in sub-optimal cost and solutions approaches. Reinforcement Learning (RL) has great potential for autonomously training agents to perform complex control tasks with minimal human intervention. Due to costly data generation and safety constraints, however, its application is mostly limited to purely simulated domains. To use RL effectively in embedded system function development, the generated agents must be able to handle real-world applications. In this context, this work focuses on accelerating the training process of RL agents by combining Transfer Learning (TL) and X-in-the-Loop (XiL) simulation. For the use case of transient exhaust gas re-circulation control for an internal combustion engine, use of a computationally cheap Model-in-the-Loop (MiL) simulation is made to select a suitable algorithm, fine-tune hyperparameters, and finally train candidate agents for the transfer. These pre-trained RL agents are then fine-tuned in a Hardware-in-the-Loop (HiL) system via TL. The transfer revealed the need for adjusting the reward parameters when advancing to real hardware. Further, the comparison between a purely HiL-trained and a transferred agent showed a reduction of training time by a factor of 5.9. The results emphasize the necessity to train RL agents with real hardware, and demonstrate that the maturity of the transferred policies affects both training time and performance, highlighting the strong synergies between TL and XiL simulation

Commitment zu aktivem Daten- und -softwaremanagement in großen Forschungsverbünden

Wir erkennen die Wichtigkeit von Forschungsdaten und -software für unsere Forschungsprozesse an und ordnen die Veröffentlichung von Forschungsdaten und -software als wesentlichen Bestandteil der wissenschaftlichen Publikationstätigkeit ein. Dafür unterstützen wir als Verbund unsere Forschenden im Umgang mit Daten und Software nach den FAIR-Prinzipien in Einvernehmen mit dem DFG-Kodex “Leitlinien zur Sicherung guter wissenschaftlicher Praxis”. In Zusammenarbeit mit unseren Institutionen und Fachcommunities stellen wir adäquate Forschungsdatenmanagement-Werkzeuge und -Dienste bereit und befähigen unsere Forschenden zum Umgang damit. Dabei bauen wir vorzugsweise auf existierenden Angeboten auf und bemühen uns im Gegenzug um deren Anpassung an unsere Bedürfnisse. Wir streben Maßnahmen für die Definition und Sicherstellung der Qualität unserer Forschungsdaten und -software an. Wir verwenden vorzugsweise existierende Daten-/Metadatenstandards und vernetzen uns nach Möglichkeit für die Erstellung und Implementierung neuer Standards mit entsprechenden nationalen und internationalen Initiativen. Wir verfolgen die Entwicklungen im Bereich des Forschungsdaten- und -softwaremanagements und prüfen neu entstehende Empfehlungen und Richtlinien zeitnah auf ihre Umsetzbarkeit

In-process detection of grinding burn using machine learning

The improvement of industrial grinding processes is driven by the objective to reduce process time and costs while maintaining required workpiece quality characteristics. One of several limiting factors is grinding burn. Usually applied techniques for workpiece burn are conducted often only for selected parts and can be time consuming. This study presents a new approach for grinding burn detection realized for each ground part under near-production conditions. Based on the in-process measurement of acoustic emission, spindle electric current, and power signals, time-frequency transforms are conducted to derive almost 900 statistical features as an input for machine learning algorithms. Using genetic programming, an optimized combination between feature selector and classifier is determined to detect grinding burn. The application of the approach results in a high classification accuracy of about 99% for the binary problem and more than 98% for the multi-classdetection case, respectively.ISSN:0268-3768ISSN:1433-301