Developers of charging infrastructure, be it public or private parties, are highly dependent on accurate utilization data in order to make informed decisions where and when to expand charging points. The Amsterdam University of Applied Sciences, in close cooperation with the municipalities of Amsterdam, Rotterdam, The Hague, Utrecht, and the Metropolitan Region of Amsterdam Electric, developed both the back- and front-end of a charging infrastructure assessment platform that processes and represents real-life charging data. Charging infrastructure planning and design methods described in the literature use geographic information system data, traffic flow data of non-EV vehicles, or geographical distributions of, for example, refueling stations for combustion engine vehicles. Only limited methods apply real-life charging data. Rolling out public charging infrastructure is a balancing act between stimulating the transition to zero-emission transport by enabling (candidate) EV drivers to charge, and limiting costly investments in public charging infrastructure. Five key performance indicators for charging infrastructure utilization are derived from literature, workshops, and discussions with practitioners. The paper describes the Data Warehouse architecture designed for processing large amounts of charging data, and the web-based assessment platform by which practitioners get access to relevant knowledge and information about the current performance of existing charging infrastructure represented by the key performance indicators developed. The platform allows stakeholders in the decision-making process of charging point installation to make informed decisions on where and how to expand the already existing charging infrastructure. The results are generalizable beyond the case study regions in the Netherlands and can serve the roll-out of charging infrastructure, both public and semi-public, all over the world.
Het belang van innovatie voor economische groei en het scheppen van werkgelegenheid in het MKB wordt erkend door zowel academici als politici. Er worden daarom programma’s ontwikkeld om innovatie te stimuleren. Met deze maatregelen ontstaat de vraag te bepalen of deze initiatieven succesvol zijn en zo ja, in welke mate. In de literatuur hebben we geen indicator gevonden die ons in staat stelt de mate van innovativiteit van MKB bedrijven te bepalen voor een dergelijke interventie en daarna. De hoofdvraag van ons onderzoek was dan ook: hoe kunnen we het effect van een interventie voor het bevorderen van de innovatiekracht van MKB-bedrijven meten? Kijkend naar de definities van innovatie zoals die zijn verzameld door King & Anderson (2002) hebben we vastgesteld dat een bedrijf innovatief genoemd mag worden als het met opzeten succesvol nieuwe ideeën implementeert. Succesvol wil in dit verband zeggen: het draagt bij aan de winst en dus aan de continuïteit van het desbetreffende MKB-bedrijf. Door de verschillende (bewuste) innovaties te identificeren samen met de ondernemer en te berekenen wat de winstgevendheid is geweest van de innovaties, kunnen we de ’innovatiewinst’ van de ondernemer berekenen. Dit bedrag delen door de omzet creëert een indicator waarmee de innovativiteit van de organisatie door de tijd gemeten kan worden. Wij stellen daarom de volgende definitie van innovatiekracht voor: KIKR = [ [Winst Innovatie1+Winst Innovatie2+ ... +Winst Innovatie5] / Omzet] x 100. De ratio kan alleen met voldoende betrouwbaarheid bepaald worden door een gestructureerd interview met de directeur/ eigenaar van de het bedrijf door een gekwalificeerde gesprekspartner. De auteurs realiseren zich dat dit gesprek op zichzelf misschien een interventie is, omdat de ervaring leert dat het innovatiebewustzijn van de ondernemer er door toeneemt. Om te bepalen of dit daadwerkelijk zo is, en om te testen of de KIKR inderdaad als bruikbare maat voor innovatiekracht kan worden gebruikt is vervolgonderzoek noodzakelijk. Desalniettemin zijn de auteurs van mening dat met de KIKR de innovatiekracht van bedrijven door de tijd heen gemeten kan worden en daarmee een bruikbaar instrument is om het effect te bepalen van interventies die innovatiekracht moeten vergroten. The importance of innovation as an engine for economic growth and the creation of employment opportunities is acknowledged by both academia and politicians. This makes the need for good innovation measures crucial. In the third edition of the Oslo Manual (2005), a need for proper indicators to capture the changes in the nature and landscape of innovation is voiced. According to the manual, a considerable body of models and analytical frameworks for innovation were developed in the 1980s and 1990s. Over time, the scope of what is considered as innovation has been widened and expanded to include marketing and organizational innovation. In this paper, we focus on innovative performance as a measure of success. This is part of ongoing research in the Netherlands in The Hague region. This research is framed within an approach based on action research. We have worked with 45 SMEs in four sectors. This has formed the basis for the conceptual development of innovative performance as a new metric for the measurement of a successful innovation. In this paper, we review our findings thus far and explore the validity of innovative performance as an appropriate indicator for measuring innovation within SMEs.
Modern engineering systems are complex socio-technical structures with a mission to offer services of high quality, while in parallel ensuring profitability for their owners. However, practice has shown that accidents are inevitable, and the need for the use of systems-theoretic tools to support safety-driven design and operation has been acknowledged. As indicated in accident investigation reports, the degradation of risk situation awareness (SA) usually leads to safety issues. However, the literature lacks a methodology to compare existing systems with their ideal composition, which is likely to enhance risk SA. To fill this gap, the risk SA provision (RiskSOAP) is a comparison-based methodology and goes through three stages: (1) determine the desired/ideal system composition, (2) identify the as-is one(s), (3) employ a comparative strategy to depict the distance between the compared units. RiskSOAP embodies three methods: STPA (System Theoretic Process Analysis), EWaSAP (Early Warning Sign Analysis) and dissimilarity measures. The practicality, applicability and generality of RiskSOAP is demonstrated through its application to three case studies. The purpose of this work is to suggest the RiskSOAP indicator as a measure for safety in terms of the gap between system design and operation, thus increasing system’s risk SA. RiskSOAP can serve as a criterion for planning system modifications or selecting between alternative systems, and can support the design, development, operation and maintenance of safe systems.
In the last decade, the automotive industry has seen significant advancements in technology (Advanced Driver Assistance Systems (ADAS) and autonomous vehicles) that presents the opportunity to improve traffic safety, efficiency, and comfort. However, the lack of drivers’ knowledge (such as risks, benefits, capabilities, limitations, and components) and confusion (i.e., multiple systems that have similar but not identical functions with different names) concerning the vehicle technology still prevails and thus, limiting the safety potential. The usual sources (such as the owner’s manual, instructions from a sales representative, online forums, and post-purchase training) do not provide adequate and sustainable knowledge to drivers concerning ADAS. Additionally, existing driving training and examinations focus mainly on unassisted driving and are practically unchanged for 30 years. Therefore, where and how drivers should obtain the necessary skills and knowledge for safely and effectively using ADAS? The proposed KIEM project AMIGO aims to create a training framework for learner drivers by combining classroom, online/virtual, and on-the-road training modules for imparting adequate knowledge and skills (such as risk assessment, handling in safety-critical and take-over transitions, and self-evaluation). AMIGO will also develop an assessment procedure to evaluate the impact of ADAS training on drivers’ skills and knowledge by defining key performance indicators (KPIs) using in-vehicle data, eye-tracking data, and subjective measures. For practical reasons, AMIGO will focus on either lane-keeping assistance (LKA) or adaptive cruise control (ACC) for framework development and testing, depending on the system availability. The insights obtained from this project will serve as a foundation for a subsequent research project, which will expand the AMIGO framework to other ADAS systems (e.g., mandatory ADAS systems in new cars from 2020 onwards) and specific driver target groups, such as the elderly and novice.
Big data spelen een steeds grotere rol in de (semi)professionele sport. De hoeveelheid gegevens die opgeslagen wordt, groeit exponentieel. Sportbegeleiders (coaches, inspanningsfysiologen, sportfysiotherapeuten en sportartsen) maken steeds vaker gebruik van sensoren om sporters te monitoren. Tijdens trainingen en wedstrijden worden de hartslagen, afgelegde afstanden, snelheden en versnellingen van sporters gemeten. Het analyseren van deze data vormt een grote uitdaging voor het begeleidingsteam van de sporters. Sportbegeleiders willen big data graag inzetten om meer grip te krijgen op sportblessures. Blessures kunnen namelijk desastreuze gevolgen hebben voor teamprestaties en de carrière van (semi)professionele sporters. In totaal stopt maar liefst 33% van de topsporters door blessures met hun sportloopbaan. Daarnaast is uitval door blessures een belangrijke oorzaak van stagnatie van talentontwikkeling. Het lectoraat Sportzorg van de Hogeschool van Amsterdam heeft veel expertise op het gebied van blessurepreventie in de sport. Sportbegeleiders hebben het lectoraat Sportzorg benaderd om antwoord te krijgen op de onderzoeksvraag: Wat zijn op data gebaseerde indicatoren om sportblessures te voorspellen? Deze onderzoeksvraagstelling is opgesplitst in de volgende deelvragen: 1. Hoe kan met sensoren relevante data van sporters verzameld worden om de sportbelasting in kaart te brengen? 2. Welke parameters kunnen blessures voorspellen? 3. Hoe kunnen deze parameters op betekenisvolle en eenvoudige wijze naar sportbegeleiders en sporters teruggekoppeld worden? Het project resulteert in de volgende projectresultaten: - Een overzicht van nauwkeurige en gebruiksvriendelijke sensoren om sportbelasting in kaart te brengen - Een overzicht van relevante parameters die blessures kunnen voorspellen - Een online tool dat per sporter aangeeft of de sporter wel of niet training- of wedstrijdfit is Bij dit project zijn de volgende organisaties betrokken: Hogeschool van Amsterdam, Universiteit Leiden, VUmc, Rijksuniversiteit Groningen (RuG), Amsterdam Institute of Sport Science (AISS), Johan Sports, Centrum voor Topsport en Onderwijs (CTO) Amsterdam, Koninklijke Nederlandse Voetbalbond (KNVB), de Nederlandse Vereniging voor Fysiotherapie in de Sport (NVFS), VV Noordwijk (voetbalclub) en Black Eagles (basketbalclub).
Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
