This study applied a decision theory approach to quantify the potential economic Value of Information (VoI) of Earth Observations (EO) based monitoring and forecasting services developed in PrimeWater project, for managing harmful algal bloom events at a recreational lake. VoI was estimated by comparing the expected costs when decisions are taken with limited information relying on regular monitoring campaigns, against the outcomes of decisions taken with "better information" conveyed by the PrimeWater services. Expected costs considered health impacts, monitoring costs, and lost recreational revenues in case of false alarms. Four PrimeWater monitoring and forecasting services were evaluated based on their accuracy in assessing bloom conditions against in-situ data for 2015-2019. Results suggest VoI varies seasonally and between services depending on accuracy metrics and underlying bloom probabilities. Forecasting solutions provide the greatest potential savings, highlighting needs for balanced metrics to avoid excess false alarms. Findings support flexible use of EO information to complement existing programs and inform science-based management aimed at reducing societal vulnerability to HABs.

The field of industrial automation has witnessed a significant increase in software development and maintenance efforts in recent years. This can be attributed to the demand for shorter product life cycles and customized products, leading to increasingly complex applications. As a result, control software engineers are facing new challenges. In this paper, we present our tool for visualizing industrial automation software. It provides engineers with a unique perspective on their large-scale applications. The tool offers several benefits, including convenient tracing of control and information flow across multiple levels of hierarchy, applying metrics to the visualization, and various interaction methods. The tool is tightly integrated into an existing IDE, providing a unique workflow for software analysis and maintenance. We discuss the experiences of engineers of our industry partner, who have been using the tool, and next steps.

Software size and complexity in all kinds of domains increase rapidly in recent times, making software maintenance and reusability increasingly difficult. Analysis of existing software and subsequent re-modularization can help with these challenges. Although software re-modularization can be time- and resource-intensive, it can support engineers in improving the quality of their systems in the future. Code can contain certain patterns, such as code smells, that define some kind of problematic implementation that is usually inefficient and hinders future development. We explore the use of a semantic clustering algorithm, which aims to minimize the code smell "Feature Envy". We apply this algorithm recursively to create a multi-level hierarchy. With this approach, we explore the re-modularization of existing IEC 61499 control systems. First tests we conducted on example implementations of a capping station and a real-life-sized industrial control system show a significant reduction of Feature Envy and thus the coupling between modules using this approach.

In recent years, the field of industrial automation is experiencing a dramatic increase of software development and maintenance effort. Driven by shorter product life-cycles and customizable products, production systems are becoming more and more complex, presenting new challenges for control software engineers. In this paper, we report on a visualization concept we applied to industrial automation software. It allows automation software developers to take a look at their software system from a new perspective. Various ways of interacting with the visualization offer further benefits, such as the convenient tracing of control and information flow across multiple hierarchies. We conducted a workshop with five industrial domain experts as a first evaluation of the usefulness of the visualization. Based on their feedback, we discuss open questions and next steps.

With the advent of the fourth industrial revolution i.e. Industry 4.0, plants and factories are becoming smarter and interconnected. The transitions demand vertical integration and seamless connectivity. For this purpose, there is a need for semantic communication between various devices including the heavily resource-constrained field devices. To address this, a real-time capable hardware-based implementation of a well-established semantic communication protocol, i.e. OPC Unified Architecture was designed and developed. This chip-based implementation is power-efficient and compact, making it suitable for the field level. The chip was analyzed and incorporated in a demonstrator as a proof of concept of its integration at field level in a plant module of the process industry. Various opportunities are also examined where the chip could be utilized to deliver benefits to existing and future technologies.

This paper extends research on TSN enablement for industrial automation, comparing the time-aware traffic shaping with the traffic (frame) preemption. Time-aware traffic shaping combined with tightly synchronized end devices enables data exchange cycles with μs granularity and nanosecond jitter, but requires system-wide scheduling for all networked devices, which can be difficult in multi-hop networks. Frame preemption does not require scheduling, and is anticipated to simplify network configuration while providing similar guarantees in latency and jitter for high priority traffic. However, it is not yet clear just how deterministic frame preemption is in comparison with time-aware traffic shaping. This manuscript compares the two mechanisms in a real heavy-loaded TSN network.

The automation of inspection tasks using robot-based inspection systems (RISs) is a complex procedure that is partly still done manually and requires expert knowledge from different fields such as systems-engineering, robotics, and metrology. We propose the use of a knowledge-based system as an elementary step to model and generalize the required knowledge to achieve the full automation of RISs. Its core component, a knowledge base for RISs, is introduced in this paper. Using a three-layer modeling approach the required expert knowledge is formalized and categorized into three different types: descriptive, procedural and relational. The knowledge base is implemented using ontologies to provide a hardware-neutral, reusable, and modular system model. Furthermore, we introduce a service-oriented architecture that uses models from the knowledge base to define the communication interfaces to ensure the data integrity and exchange between the knowledge base and all software modules of an RIS (e.g., robot and sensor control, user-interface, and simulation software). The validation of the proposed approach is shown by using the example of an industrial RIS.

The latest developments and research of convolutional neuronal networks (CNNs) have proven the feasibility of their use in industrial applications that require object detection and pose estimation in unknown environments. Nevertheless, the end-users have neither the required resources for model-training nor the expertise to efficiently implement such applications. On the one hand, our work proposes a pipeline that focuses on the automated generation of training data by using synthetic images. On the other hand, we introduce a deep neural network to estimate the orientation of a reference object by using a one-shot image. We demonstrate the use of PoseNetwork by detecting and estimating the 5D-Pose of a workpiece in a robot-based inspection cell.

This paper extends research on TSN enablement for OPC UA field devices, comparing the impact of credit-based and time-aware traffic shapers. Despite conceptual difference, these traffic shaping methods can provide similar bandwidth reservation for high priority traffic. The two shapers are superposed with varying traffic limitations to assess the timing characteristics of the OPC UA data exchange using a resource-constrained field device.

In a field test at Saarland University, a decision tool named OWLracle has been developed. It supports university managers in comparing benefit and costs of different methods of energy saving. They include building automation as well as economic and psychological incentives. In order to guarantee transferability to other university properties, appropriate typicals are required. To this end, the paper defines common Building Types, Room Types, and User Types, representative of German universities. Furthermore, it introduces a BCVTB-based implementation of OWLracle, integrating Modélica® libraries of Room and User Types and EnergyPlus models of Building Types. 27 combinations of Building, Room and User Type are compared with respect to their total energy consumption.