Assigning gates to flights considering physical, operational, and temporal constraints is known as the Gate Assignment Problem. This article proposes the novelty of coupling a commercial stand and gate allocation software with an off-the-grid optimization algorithm. The software provides the assignment costs, verifies constraints and restrictions of an airport, and provides an initial allocation solution. The gate assignment problem was solved using a genetic algorithm. To improve the robustness of the allocation results, delays and early arrivals are predicted using a random forest regressor, a machine learning technique and in turn they are considered by the optimization algorithm. Weather data and schedules were obtained from Zurich International Airport. Results showed that the combination of the techniques result in more efficient and robust solutions with higher degree of applicability than the one possible with the sole use of them independently.
Optimal postural control is an essential capacity in daily life and can be highly variable. The purpose of this study was to investigate if young people have the ability to choose the optimal postural control strategy according to the postural condition and to investigate if non-specific low back pain (NSLBP) influences the variability in proprioceptive postural control strategies. Young individuals with NSLBP (n = 106) and healthy controls (n = 50) were tested on a force plate in different postural conditions (i.e., sitting, stable support standing and unstable support standing). The role of proprioception in postural control was directly examined by means of muscle vibration on triceps surae and lumbar multifidus muscles. Root mean square and mean displacements of the center of pressure were recorded during the different trials. To appraise the proprioceptive postural control strategy, the relative proprioceptive weighting (RPW, ratio of ankle muscles proprioceptive inputs vs. back muscles proprioceptive inputs) was calculated. Postural robustness was significantly less in individuals with NSLBP during the more complex postural conditions (p < 0.05). Significantly higher RPW values were observed in the NSLBP group in all postural conditions (p < 0.05), suggesting less ability to rely on back muscle proprioceptive inputs for postural control. Therefore, healthy controls seem to have the ability to choose a more optimal postural control strategy according to the postural condition. In contrast, young people with NSLBP showed a reduced capacity to switch to a more multi-segmental postural control strategy during complex postural conditions, which leads to decreased postural robustness.
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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.
MULTIFILE
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
Aanleiding: Automatisering kan leiden tot beter gebruik van materialen en afval reduceren. Dit brengt verbeteringen met zich mee voor 'people, planet and profit' (PPP) - mensen, het milieu en de winst. Een specifieke vorm van automatisering, de ontwikkeling van zelfrijdende auto's en vrachtauto's, gaat snel. Maar om zelfrijdende voertuigen beschikbaar te maken is er nog veel onderzoek en ontwikkeling nodig op verschillende gebieden. Er zijn nog veel vragen te beantwoorden op het gebied van onder andere truckontwerp, betrouwbare software, aansprakelijkheid, trajectplanning en logistiek. Doelstelling Het doel van het Intralog-project is om voor de maatschappij en de private sector een significante bijdrage te leveren aan de mogelijkheden van zelfrijdende voertuigen in de commerciële transportsector. Het Intralog-project onderzoekt de toegevoegde waarde voor PPP van 'automated guided trucks' (AGT's) aan logistieke operaties bij distributiecentra en interterminal/intermodal traffic hubs. Dit gebeurt in twee stappen: 1) het identificeren van het potentieel met betrekking tot de vraag vanuit de logistieke omgeving; 2. het ontwerpen, realiseren, testen en valideren van mogelijke strategieën voor het implementeren van AGT's in een logistiek scenario. Beoogde resultaten Het concrete resultaat van het project bestaat uit onderzoekstools en hardware- en softwaremodellen voor Intralog. Deze bieden een goede mogelijkheid om de opgedane kennis te verspreiden. De projectdeelnemers zullen bijdragen aan workshops, tentoonstellingen en in Nederland georganiseerde symposia. De onderzoeksresultaten verspreiden ze op conferenties en door middel van publicaties in technische vakbladen. De uiteindelijke Intralog-resultaten worden gepresenteerd op een afsluitend congres. De resultaten zullen worden samengevat in een boekje.
The demand for mobile agents in industrial environments to perform various tasks is growing tremendously in recent years. However, changing environments, security considerations and robustness against failure are major persistent challenges autonomous agents have to face when operating alongside other mobile agents. Currently, such problems remain largely unsolved. Collaborative multi-platform Cyber- Physical-Systems (CPSs) in which different agents flexibly contribute with their relative equipment and capabilities forming a symbiotic network solving multiple objectives simultaneously are highly desirable. Our proposed SMART-AGENTS platform will enable flexibility and modularity providing multi-objective solutions, demonstrated in two industrial domains: logistics (cycle-counting in warehouses) and agriculture (pest and disease identification in greenhouses). Aerial vehicles are limited in their computational power due to weight limitations but offer large mobility to provide access to otherwise unreachable places and an “eagle eye” to inform about terrain, obstacles by taking pictures and videos. Specialized autonomous agents carrying optical sensors will enable disease classification and product recognition improving green- and warehouse productivity. Newly developed micro-electromechanical systems (MEMS) sensor arrays will create 3D flow-based images of surroundings even in dark and hazy conditions contributing to the multi-sensor system, including cameras, wireless signatures and magnetic field information shared among the symbiotic fleet. Integration of mobile systems, such as smart phones, which are not explicitly controlled, will provide valuable information about human as well as equipment movement in the environment by generating data from relative positioning sensors, such as wireless and magnetic signatures. Newly developed algorithms will enable robust autonomous navigation and control of the fleet in dynamic environments incorporating the multi-sensor data generated by the variety of mobile actors. The proposed SMART-AGENTS platform will use real-time 5G communication and edge computing providing new organizational structures to cope with scalability and integration of multiple devices/agents. It will enable a symbiosis of the complementary CPSs using a combination of equipment yielding efficiency and versatility of operation.
