The Rockefeller Brothers Fund's Western Balkans Program: Midterm Impact Assessment

The Rockefeller Brothers Fund (RBF) commissioned an impact assessment of its Western Balkans program from 2010 to 2015. As the team who carried out this assessment, our overall conclusion from the assessment is that the RBF program in the Western Balkans is having meaningful positive impact, and it is relevant to the developments in Serbia, Montenegro, Kosovo, and the rest of the region. We believe the program is well designed and is achieving a lot with a relatively small amount of money

Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task

This paper presents a comparison between two well-known deep Reinforcement Learning (RL) algorithms: Deep Q-Learning (DQN) and Proximal Policy Optimization (PPO) in a simulated production system. We utilize a Petri Net (PN)-based simulation environment, which was previously proposed in related work. The performance of the two algorithms is compared based on several evaluation metrics, including average percentage of correctly assembled and sorted products, average episode length, and percentage of successful episodes. The results show that PPO outperforms DQN in terms of all evaluation metrics. The study highlights the advantages of policy-based algorithms in problems with high-dimensional state and action spaces. The study contributes to the field of deep RL in context of production systems by providing insights into the effectiveness of different algorithms and their suitability for different tasks.Comment: Submitted and accepted version to the 32nd International Symposium on Industrial Electronics (ISIE), Helsinki, Finlan

Misuratore laser a triangolazione a banda larga

LAUREA SPECIALISTIC

A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications

This application paper explores the potential of using reinforcement learning (RL) to address the demands of Industry 4.0, including shorter time-to-market, mass customization, and batch size one production. Specifically, we present a use case in which the task is to transport and assemble goods through a model factory following predefined rules. Each simulation run involves placing a specific number of goods of random color at the entry point. The objective is to transport the goods to the assembly station, where two rivets are installed in each product, connecting the upper part to the lower part. Following the installation of rivets, blue products must be transported to the exit, while green products are to be transported to storage. The study focuses on the application of reinforcement learning techniques to address this problem and improve the efficiency of the production process.Comment: Submitted and accepted version to the 5th International Data Science Conference (iDSC), Krems, Austri

An Architecture for Deploying Reinforcement Learning in Industrial Environments

Industry 4.0 is driven by demands like shorter time-to-market, mass customization of products, and batch size one production. Reinforcement Learning (RL), a machine learning paradigm shown to possess a great potential in improving and surpassing human level performance in numerous complex tasks, allows coping with the mentioned demands. In this paper, we present an OPC UA based Operational Technology (OT)-aware RL architecture, which extends the standard RL setting, combining it with the setting of digital twins. Moreover, we define an OPC UA information model allowing for a generalized plug-and-play like approach for exchanging the RL agent used. In conclusion, we demonstrate and evaluate the architecture, by creating a proof of concept. By means of solving a toy example, we show that this architecture can be used to determine the optimal policy using a real control system.Comment: This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution is published in Computer Aided Systems Theory - EUROCAST 2022 and is available online at https://doi.org/10.1007/978-3-031-25312-6_6

Distribution of oxygen consumption by graded loads during ergonometric testing

Cardiopulmonary exercise monitoring is a valuable method not only for the evaluation of medical health, but also for the assessment of patients with cardiac or pulmonary dysfunction. Spiroergometry provides additional criteria for the assessment of cardiopulmonary efficiency compared to simple ergometry. Maximal oxygen consumption (VO2max) is the most critical variable during spiroergometry. Most submaximal exercise measures provide the heart rate (HR) response to predetermined workloads in equations or nomograms used to predict VO2max. According to previous studies, the heart rate is divided into five fields. In this paper, we are doing a new redistribution of heart rates-to-workloads into seven fields, corresponding to the ergo bar. In other words, an answer is given based on the initial anthropological values of the subjects, when and in which field there will be a mismatch between the lung capacity of the subjects and the power required for that field

The optimal design of school desks depending on the height and weight of students

Background: The subject of this research is the creation of an optimal school bench design with the aim of determining the most favorable posture of students while sitting, taking into account the relevant ergonometric and biomechanical characteristics of the human body. For the proposed model of the school bench which allows adjusting the different slopes of its surface, the corresponding computer model of the student and the table was first created, and then biomechanical and RULA analysis was performed in order to determine the maximum load in the lumbar part. Next, for each test subject of given weight, it was necessary to determine the amount of maximum load in lumbar zone L3/L4 for different slope angles and to determine the critical angles at which the maximum permissible load of 3400 N is reached. Methods: The analysis is performed on a total of 5 subjects of the same height (180 cm) and various weights (60, 70, 80, 90, 100 kg). The task is to determine at which weight and at what angle of the workbench with standard height will not exceed the permissible loads of the spine, specifically referring to the L4/L5 vertebrae whose stresses should not exceed 3400 N. The CATIA software package (Dassault Systèmes, Vélizy-Villacoublay, France) is used for the analysis. By knowing the anthropometric and work environment data with ergonomic design and analysis, the following analyzes were made: biomechanical analysis, rapid upper limb assessment (RULA) and carry analysis (an option from CATIA software). Results: The proposed school bench design allows for flexible adjustments to its worktop, that is, changing its tilt. This allows students of different body masses to have an optimal position at work that does not compromise their maximum permissible load in the L4/L5 spinal column (3400N). Conclusions: The proposed ergonomic design of the desk will result in students being adequately positioned during their activities at school with the minimal risk of permanent deviations and other health problems