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Data Driven Design: Ship Engineering Application D³:SEA

The maritime transport industry is facing a series of challenges due to the phasing out of fossil fuels and the challenges from decarbonization. The proposal of proper alternatives is not a straightforward process. While the current generation of ship design software offers results, there is a clear missed potential in new software technologies like machine learning and data science. This leads to the question: how can we use modern computational technologies like data analysis and machine learning to enhance the ship design process, considering the tools from the wider industry and the industry’s readiness to embrace new technologies and solutions? The obbjective of this PD project is to bridge the critical gap between the maritime industry's pressing need for innovative solutions for a more agile Ship Design Process; and the current limitations in software tools and methodologies available via the implementation into Ship Design specific software of the new generation of computational technologies available, as big data science and machine learning.

Data mining for MRO process optimization

In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.

De ontwikkeling van een nieuwe tool voor praktijkgericht sensorisch- en consumentenonderzoek met groenteproducten

In de ontwikkeling van voedingsmiddelen draait alles om smaak. Eten moeten vooral lekker zijn, toch? Om smaak en andere producteigenschappen in kaart te brengen wordt sensorisch onderzoek (SO) uitgevoerd. In de praktijk gebeurt dit vaak onder suboptimale omstandigheden vanwege bijvoorbeeld onvoldoende beschikbaarheid van panelleden en een gebrek aan tijd. Belangrijke beslissingen worden vervolgens gebaseerd op onbetrouwbare, invalide resultaten. Er is daarom een behoefte naar snelle, betrouwbare en valide methodieken. Daarbij geldt dat voedselkeuze een complex proces dat verder gaat dat het analytisch beoordelen van producteigenschappen. Zo kan de context van consumptie van invloed zijn op de perceptie van smaak. In het sensorisch onderzoeksveld is hier steeds meer aandacht voor. Hiermee beweegt SO zich richting consumentenonderzoek (CO). De overlap en wederzijdse beïnvloeding van elementen uit SO en CO zijn relevant tijdens verschillende fases van het productontwikkelingsproces. In dit postdoconderzoek zal Vera van Stokkom een snelle en flexibele onderzoeksmethode ontwikkelen en valideren. Elementen van SO en CO zullen geïntegreerd worden in een methodiek onder de werktitel Rapid Sensory & Consumer Modules Tool (RSCM-tool). De methodiek zal worden gericht op de productcategorie groenten. Groenten passen bij uitstek in een gezond en duurzaam voedingspatroon. Ook zal worden onderzocht of de RSCM-tool kan worden ingezet bij ouderen, omdat bij ouderen achteruitgang in sensorische perceptie plaatsvindt. Met dit postdoconderzoek wordt kennis en ervaring opgedaan over de toepassing van een nieuwe sensorische onderzoeksmethodiek (RSCM) die uiterst relevant is voor onderzoek met, en onderwijs op het gebied van voeding. Dit postdoconderzoek valt onder het Research and Innovation Centre Agri, Food & Life Sciences van Hogeschool Inholland. Naast een verbreding en verdieping op de onderzoekslijn Gezondheid in de Metropool en het domeinoverstijgende onderzoeksprogramma Food for Happy Ageing, zal dit onderzoek bijdragen aan duurzame curriculuminnovatie binnen de opleiding Food Commerce and Technology en de profilering van Hogeschool Inholland als kennispartner.

Defending against Implementation attackS Through mAchiNe learning Techniques

Today, embedded devices such as banking/transportation cards, car keys, and mobile phones use cryptographic techniques to protect personal information and communication. Such devices are increasingly becoming the targets of attacks trying to capture the underlying secret information, e.g., cryptographic keys. Attacks not targeting the cryptographic algorithm but its implementation are especially devastating and the best-known examples are so-called side-channel and fault injection attacks. Such attacks, often jointly coined as physical (implementation) attacks, are difficult to preclude and if the key (or other data) is recovered the device is useless. To mitigate such attacks, security evaluators use the same techniques as attackers and look for possible weaknesses in order to “fix” them before deployment. Unfortunately, the attackers’ resourcefulness on the one hand and usually a short amount of time the security evaluators have (and human errors factor) on the other hand, makes this not a fair race. Consequently, researchers are looking into possible ways of making security evaluations more reliable and faster. To that end, machine learning techniques showed to be a viable candidate although the challenge is far from solved. Our project aims at the development of automatic frameworks able to assess various potential side-channel and fault injection threats coming from diverse sources. Such systems will enable security evaluators, and above all companies producing chips for security applications, an option to find the potential weaknesses early and to assess the trade-off between making the product more secure versus making the product more implementation-friendly. To this end, we plan to use machine learning techniques coupled with novel techniques not explored before for side-channel and fault analysis. In addition, we will design new techniques specially tailored to improve the performance of this evaluation process. Our research fills the gap between what is known in academia on physical attacks and what is needed in the industry to prevent such attacks. In the end, once our frameworks become operational, they could be also a useful tool for mitigating other types of threats like ransomware or rootkits.

Dependency analysis tool MSP Challenge.

DevelopIng STratEgies by integrating mitigatioN, aDaptation and participation to climate changE Risks

DISTENDER will provide integrated strategies by building a methodological framework that guide the integration of climate change(CC) adaptation and mitigation strategies through participatory approaches in ways that respond to the impacts and risks of climatechange (CC), supported by quantitative and qualitative analysis that facilitates the understanding of interactions, synergies and tradeoffs.Holistic approaches to mitigation and adaptation must be tailored to the context-specific situation and this requires a flexibleand participatory planning process to ensure legitimate and salient action, carried out by all important stakeholders. DISTENDER willdevelop a set of multi-driver qualitative and quantitative socio-economic-climate scenarios through a facilitated participatory processthat integrates bottom-up knowledge and locally-relevant drivers with top-down information from the global European SharedSocioeconomic Pathways (SSPs) and downscaled Representative Concentration Pathways (RCPs) from IPCC. A cross-sectorial andmulti-scale impact assessment modelling toolkit will be developed to analyse the complex interactions over multiple sectors,including an economic evaluation framework. The economic impact of the different efforts will be analyse, including damage claimsettlement and how do sectoral activity patterns change under various scenarios considering indirect and cascading effects. It is aninnovative project combining three key concepts: cross-scale, integration/harmonization and robustness checking. DISTENDER willfollow a pragmatic approach applying methodologies and toolkits across a range of European case studies (six core case studies andfive followers) that reflect a cross-section of the challenges posed by CC adaptation and mitigation. The knowledge generated byDISTENDER will be offered by a Decision Support System (DSS) which will include guidelines, manuals, easy-to-use tools andexperiences from the application of the cases studies.