The methodology should be a uniform approach that also is flexible enough to accommodate all combinations that make up the different solutions in 6 OPs. For KPIs A and B this required the use of sub-KPIs to differentiate the effects of each (individual and combination of) implemented solutions and prevent double counting of results. This approach also helped to ensure that all 6 OPs use a common way and scope to calculate the various results. Consequently, this allowed the project to capture the results per OP and the total project in one ‘measurement results’ template. The template is used in both the individual OP reports and the ‘KPI Results: Baseline & Final results’ report where all results are accumulated; each instance providing a clear overview of what is achieved. This report outlines the details of the methodology used and applied. It is not just meant to provide a clarification of the results of the project, but is also meant to allow others who are embarking on adopting similar solutions for the purpose of CO2 reduction, becoming more energy autonomous or avoid grid stress or investments to learn about and possibly use the same methodology.
Martijn de Waal bezocht op uitnodiging van Het Nieuwe Instituut de opening van de achtste editie van de architectuurbiënnale van Shenzhen. Naar aanleiding van zijn bezoek aan de tentoonstelling Eyes of the City schrijft hij over hoe de opkomst van camera's, AI en deep learning het leven in de stad verandert.
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Many lithographically created optical components, such as photonic crystals, require the creation of periodically repeated structures [1]. The optical properties depend critically on the consistency of the shape and periodicity of the repeated structure. At the same time, the structure and its period may be similar to, or substantially below that of the optical diffraction limit, making inspection with optical microscopy difficult. Inspection tools must be able to scan an entire wafer (300 mm diameter), and identify wafers that fail to meet specifications rapidly. However, high resolution, and high throughput are often difficult to achieve simultaneously, and a compromise must be made. TeraNova is developing an optical inspection tool that can rapidly image features on wafers. Their product relies on (a) knowledge of what the features should be, and (b) a detailed and accurate model of light diffraction from the wafer surface. This combination allows deviations from features to be identified by modifying the model of the surface features until the calculated diffraction pattern matches the observed pattern. This form of microscopy—known as Fourier microscopy—has the potential to be very rapid and highly accurate. However, the solver, which calculates the wafer features from the diffraction pattern, must be very rapid and precise. To achieve this, a hardware solver will be implemented. The hardware solver must be combined with mechatronic tracking of the absolute wafer position, requiring the automatic identification of fiduciary markers. Finally, the problem of computer obsolescence in instrumentation (resulting in security weaknesses) will also be addressed by combining the digital hardware and software into a system-on-a-chip (SoC) to provide a powerful, yet secure operating environment for the microscope software.
Een vraagarticulatieproces met projectmanagers en -leiders uit private en Triple-Helix organisaties laat zien dat zij behoefte hebben aan tools voor: 1. Het bepalen van de juiste incentives om stakeholders actief te betrekken in multi-sector collaboratieve innovatieprojecten (verder verwezen als innovatieprojecten), en 2. Het concreet, transparant en op één lijn te krijgen van de belangen van de partners. Vandaar dat dit project betreft het doorontwikkelen van het Degrees of Engagement diagram (DoE-diagram), een tool voor het managen van stakeholder engagement in innovatieprojecten voor het behalen van de maatschappelijke opgaven. Hiermee sluit het project aan bij de programmalijn ‘rollen, belangen en coördinatie’ van de Kennis en Innovatieagenda van de missie Maatschappelijke Verdienvermogen- thema’s Klimaat & Energie en Circulaire economie. Het consortium bestaat uit de Hogeschool van Amsterdam (HvA), KplusV en Amsterdam Smart City (ASC). De HvA ontwikkelde het DoE-diagram. Voor het identificeren van stakeholders bevat het DoE-diagram attributen op project- en organisatieniveau. In dit project wordt het DoE doorontwikkeld door onderzoek te doen naar: 1. De attributen op individuniveau en potentiele nieuwe attributen op project- en organisatieniveau, 2. De mate waarin deze attributen invloed hebben op het bepalen van de passende incentives, de concretisering van de partnerbelangen en al dan niet succesvolle verloop van innovatieprojecten, 3. Een verkenning van een digitale versie van het DoE voor het managen van in- en uitstappen van partners. Hiermee beoogt het project twee doelen: 1. Inzicht verkrijgen in stakeholderconfiguraties voor het ondersteunen van beslissingen met betrekking tot stakeholder-engagement, 2. Bouwen van een consortium van partijen die vervolg aan het project gaan geven door longitudinaal onderzoek te doen naar de inzet van de uitbreiding van het DoE-diagram en het maken van een werkend prototype en testen van de digitale versie ervan.
The maximum capacity of the road infrastructure is being reached due to the number of vehicles that are being introduced on Dutch roads each day. One of the plausible solutions to tackle congestion could be efficient and effective use of road infrastructure using modern technologies such as cooperative mobility. Cooperative mobility relies majorly on big data that is generated potentially by millions of vehicles that are travelling on the road. But how can this data be generated? Modern vehicles already contain a host of sensors that are required for its operation. This data is typically circulated within an automobile via the CAN bus and can in-principle be shared with the outside world considering the privacy aspects of data sharing. The main problem is, however, the difficulty in interpreting this data. This is mainly because the configuration of this data varies between manufacturers and vehicle models and have not been standardized by the manufacturers. Signals from the CAN bus could be manually reverse engineered, but this process is extremely labour-intensive and time-consuming. In this project we investigate if an intelligent tool or specific test procedures could be developed to extract CAN messages and their composition efficiently irrespective of vehicle brand and type. This would lay the foundations that are required to generate big data-sets from in-vehicle data efficiently.
