Abstract Despite the numerous business benefits of data science, the number of data science models in production is limited. Data science model deployment presents many challenges and many organisations have little model deployment knowledge. This research studied five model deployments in a Dutch government organisation. The study revealed that as a result of model deployment a data science subprocess is added into the target business process, the model itself can be adapted, model maintenance is incorporated in the model development process and a feedback loop is established between the target business process and the model development process. These model deployment effects and the related deployment challenges are different in strategic and operational target business processes. Based on these findings, guidelines are formulated which can form a basis for future principles how to successfully deploy data science models. Organisations can use these guidelines as suggestions to solve their own model deployment challenges.
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Since the first uptake of electric vehicles, policy makers are questioning how to rollout public charging infrastructure in an efficient manner, such that user convenience balances with costs of investment. In some metropolitan areas, the first phase of rollout has been passed, meaning an optimized deployment of future charging stations for electric vehicles (EVs) becomes important to improve the charging infrastructure and ensure customer satisfaction and sufficient service provision. Complex system literature shows that network vulnerability is an important metric, yet, charging infrastructure has not yet been a subject of these simulation models so far. This research, based on real-world data, provides a novel approach for improving the roll-out strategy of municipalities, by treating the charge infrastructure as a complex network of charging stations and defining vulnerability in respect to the availability of its surrounding charging stations within relevant walking distance.
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At the department of electrical and electronic engineering of Fontys University of Applied Sciences we are defining a real-life learning context for our students, where the crossover with regional healthcare companies and institutes is maximized. Our innovative educational step is based on openly sharing electronic designs for health related measurement modalities as developed by our students. Because we develop relevant reference designs, the cross fertilization with society is large and so the learning cycle is short.
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The consistent demand for improving products working in a real-time environment is increasing, given the rise in system complexity and urge to constantly optimize the system. One such problem faced by the component supplier is to ensure their product viability under various conditions. Suppliers are at times dependent on the client’s hardware to perform full system level testing and verify own product behaviour under real circumstances. This slows down the development cycle due to dependency on client’s hardware, complexity and safety risks involved with real hardware. Moreover, in the expanding market serving multiple clients with different requirements can be challenging. This is also one of the challenges faced by HyMove, who are the manufacturer of Hydrogen fuel cells module (https://www.hymove.nl/). To match this expectation, it starts with understanding the component behaviour. Hardware in the loop (HIL) is a technique used in development and testing of the real-time systems across various engineering domain. It is a virtual simulation testing method, where a virtual simulation environment, that mimics real-world scenarios, around the physical hardware component is created, allowing for a detailed evaluation of the system’s behaviour. These methods play a vital role in assessing the functionality, robustness and reliability of systems before their deployment. Testing in a controlled environment helps understand system’s behaviour, identify potential issues, reduce risk, refine controls and accelerate the development cycle. The goal is to incorporate the fuel cell system in HIL environment to understand it’s potential in various real-time scenarios for hybrid drivelines and suggest secondary power source sizing, to consolidate appropriate hybridization ratio, along with optimizing the driveline controls. As this is a concept with wider application, this proposal is seen as the starting point for more follow-up research. To this end, a student project is already carried out on steering column as HIL
With increasing labor shortages, sectors using mobile machines (automotive/industry/agrifood/logistics) have a rising need for productivity improvement. With evolving technology, mobile machine control has stepped from hydraulics to electronics using sensors and smart systems to support drivers and allowing intelligent and automated machine functions. Verification and validation costs of such complex functionality urge the need for virtual solution routes to limit the lead time, cost and safety issues of real-world testing. RAAK-mkb project Fast&Curious developed tools to enable model-driven development for the control of a wide range of vehicle systems. This included automatic code generation support from MATLAB/Simulink® into the Bodas RC30 family vehicle controllers from Bosch Rexroth (see www.openMBD.com). The solution has been adopted by several SMEs allowing them to start working in a model-driven way, helping them to do virtual verification&validation, lowering development time and costs. Meanwhile, Rexroth adopted MATLAB/Simulink for core vehicle functions development and currently develops Fast&Curious-alike automatic code generation support for their recent RC40 controllers. Virtuoso aims to further improve productivity on simulation level by creating an interface layer in Simulink to (automatically) test impact of hardware interface imperfections and failures, such as noise and short circuits, as well as to seamlessly switch between continuous (early development) and discretized (deployment-oriented) input/output behavior. Companies like Emoss and Jautomatisering are interested in such solutions, allowing them to adopt efficient, model-driven processes and supporting their engineers in the required hydraulics-to-software/electronics skill-shift. The solution connects well to future developments like robotization. Besides supporting development of vehicle automation and mobile robotics, MATLAB/Simulink also supports ROS (Robot Operating System) via co-simulation and co-deployment. ROS has become the standard in (mobile) robot control development and is used by many parties. Virtuoso further closes the gap between development and deployment and allows future integration in mobile robotics, foreseen as next step.
