Gepubliceerd in Mikroniek, nr. 6 2018 In manufacturing environments where collaborative robots are employed, conventional computer vision algorithms have trouble in the robust localisation and detection of products due to changing illumination conditions and shadows caused by a human sharing the workspace with the robotic system. In order to enhance the robustness of vision applications, machine learning with neural networks is explored. The performance of machine-learning algorithms versus conventional computer vision algorithms is studied by observing a generic user scenario for the manufacturing process: the assembly of a product by localisation, identification and manipulation of building blocks.
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The aviation industry needs led to an increase in the number of aircraft in the sky. When the number of flights within an airspace increases, the chance of a mid-air collision increases. Systems such as the Traffic Alert and Collision Avoidance System (TCAS) and Airborne Collision Avoidance System (ACAS) are currently used to alert pilots for potential mid-air collisions. The TCAS and the ACAS use algorithms to perform Aircraft Trajectory Predictions (ATPs) to detect potential conflicts between aircrafts. In this paper, three different aircraft trajectory prediction algorithms named Deep Neural Network (DNN), Random Forest (RF) and Extreme Gradient Boosting were implemented and evaluated in terms of their accuracy and robustness to predict the future aircraft heading. These algorithms were as well evaluated in the case of adversarial samples. Adversarial training is applied as defense method in order to increase the robustness of ATPs algorithms against the adversarial samples. Results showed that, comparing the three algorithm’s performance, the extreme gradient boosting algorithm was the most robust against adversarial samples and adversarial training may benefit the robustness of the algorithms against lower intense adversarial samples. The contributions of this paper concern the evaluation of different aircraft trajectory prediction algorithms, the exploration of the effects of adversarial attacks, and the effect of the defense against adversarial samples with low perturbation compared to no defense mechanism.
According to the "membrane sensor" hypothesis, the membrane's physical properties and microdomain organization play an initiating role in the heat shock response. Clinical conditions such as cancer, diabetes and neurodegenerative diseases are all coupled with specific changes in the physical state and lipid composition of cellular membranes and characterized by altered heat shock protein levels in cells suggesting that these "membrane defects" can cause suboptimal hsp-gene expression. Such observations provide a new rationale for the introduction of novel, heat shock protein modulating drug candidates. Intercalating compounds can be used to alter membrane properties and by doing so normalize dysregulated expression of heat shock proteins, resulting in a beneficial therapeutic effect for reversing the pathological impact of disease. The membrane (and lipid) interacting hydroximic acid (HA) derivatives discussed in this review physiologically restore the heat shock protein stress response, creating a new class of "membrane-lipid therapy" pharmaceuticals. The diseases that HA derivatives potentially target are diverse and include, among others, insulin resistance and diabetes, neuropathy, atrial fibrillation, and amyotrophic lateral sclerosis. At a molecular level HA derivatives are broad spectrum, multi-target compounds as they fluidize yet stabilize membranes and remodel their lipid rafts while otherwise acting as PARP inhibitors. The HA derivatives have the potential to ameliorate disparate conditions, whether of acute or chronic nature. Many of these diseases presently are either untreatable or inadequately treated with currently available pharmaceuticals. Ultimately, the HA derivatives promise to play a major role in future pharmacotherapy.
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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.
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
Groenvermogen is een nationaal groeifonds programma dat de waardeketen van waterstof wil ontwikkelen. In WP3 wordt er in een consortium gekeken naar toepassingen van waterstof. The direct use of hydrogen in various sectors shares common challenges and needs to accelerate its deployment and reduce its costs. Firstly, there is a need for extensive research and development to: - Maximize energy efficiency with minimal pollutant emissions; - Maximize robustness by meeting dynamic performance requirements (especially linked to mobility and local integrated energy systems with intermittent renewable energy generation or energy demand); - Enable a gradual fuel transition and therefore focus on fuel-flexible technologies; - Shorten time-to market of green hydrogen technology - Maximize the life time of energy conversion technologies; - Reduce investment costs.
