Real-World Example. Line-Following Robot In this section we demonstrate how to apply our analysis to an existing real-world example, written in C. The program is simple enough that there is no potential for energy consumption optimization, but nonetheless our analysis gives insight into the program’s energy behaviour. We chose a random “simple line follower” project from the Arduino project database [1]. This robot has two motors and two sensors, and uses them to follow a black line on the floor. It works as follows. The sensors are positioned to the left and right of the line. If only the left sensor sees the line, the robot turns left. Symmetrically for the right sensor. If neither sensor sees the line, the robot moves forward. If both sensors see the line, the robot stops. The code has potential for refactoring, as it contains unnecessary repetition. However our goal was not to find the most elegant line follower robot, but to apply our method to a real-world example. The original source code, written in C, is almost valid SECA. We made two changes to the code for our parser to accept it. First, we defined the constants LOW and HIGH, and the function delay, which for our purpose is empty. Second, we replaced the statements that write to output pins and read from input pins with component calls. Figure 13 shows an excerpt of the code after these adjustments. We then created the component models for motors and sensors in the source code of our analysis engine. The simulated motors have three states, forward, backward, and stop, and corresponding component calls. In the forward and backward states, motors have a power draw of 750 mW. The sensors have no power draw, and their read component call returns a symbolic value in {0, 1}. Analysis Results. Figure 14 shows skyline diagrams for the functions loop (top) and MoveForward (bottom). The diagram for MoveForward illustrates that the function has two behaviours. One where the power draw increases in two steps from 0 to 1500 mW, and one where the power draw stays constant at 1500 mW. The functions TurnLeft and TurnRight, not shown here, look similar. The func- tion Stop, also not shown, has the opposite behaviour: the power draw decreases 1 int main() { 2 while( true ) { 3 loop(); 4 } 5 return 0; 6 } 8 if((XxxxxxXxxx.xxxx()==LOW) && 9 (XxxxxxXxxxx.xxxx()==LOW)) { 10 MoveForward(); 11 } 13 (XxxxxxXxxxx.xxxx()==HIGH)) { 14 Stop();
Real-World Example. Furniture Pilot 48 A.2 Real World Example – Circular Economy Pilot 54 Bibliography 59 European foreword This CEN and CENELEC Workshop Agreement has been developed in accordance with the CEN-CENELEC Guide 29 “CEN/CENELEC Workshop Agreements – A rapid prototyping to standardization” and with the relevant provisions of CEN/CENELEC Internal Regulations - Part 2. It was approved by a Workshop of representatives of interested parties on YYYY-MM-DD, the constitution of which was supported by CEN and CENELEC following the public call for participation made on 2020-12-16. However, this CEN and CENELEC Workshop Agreement does not necessarily include all relevant stakeholders. The final text of this CEN and CENELEC Workshop Agreement was provided to CEN and CENELEC for publication on YYYY-MM-DD. Results incorporated in this CWA received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825075. The following organizations and individuals developed and approved this CEN and CENELEC Workshop Agreement: • 3D ICOM GmbH & Co. KG • AM Allied Maintenance GmbH • Ascora GmbH • Austrian Standards International • Brimatech Services GmbH • Caixa Mágica Software SA • The Centre for Research & Technology, Hellas - CERTH • DIN Deutsches Institut für Normung e. V. • Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e. V. • Hanse Aerospace Wirtschaftsdienst GmbH • Information Catalyst for Enterprise Ltd.
Real-World Example. Furniture Pilot User Story: Production optimisation. Real implementation in the industrial shopfloor of LAGRAMA (Vinaros, Spain).
Real-World Example. Circular Economy Pilot User Story: Predictive Maintenance Solution for Lift Manufacturer XXXXXXXX’s Polishing Machine, Kilkis – Greece Description: This user story focuses on the early detection of machine failures in the polishing machine at KLEEMANN’s shopfloor. The sensors installed are used to capture vibration data. In order to achieve this, a solution is needed to predict potential machine defects, so that the Maintenance Manager can improve maintenance operations and procedures. The Predictive Maintenance solution was implemented by using EFPF components such as • Vibration sensors connected over Wi-Fi in KLEEMANN production line • Data Spine’s Message Bus is used as the secure broker for publishing vibration sensors data in order to be used by analytics components • Data Spine’s Security Framework is used in order to provide access to the solution and its data only to authorized users from KLEEMANN • EFPF Portal is used in this case as well, in order to direct user to the solution’s interfaces • Visual and Data Analytics tool is used for the delivery of the predictive maintenance solution in terms of both data analysis and real time monitoring Architecture: <.. image(Diagram Description automatically generated) removed ..>
