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Producten 198

product

The role of Edge/Fog computing security in IoT and Industry 4.0 infrastructures

The development of information and communication technologies (ICT) has led to many innovative technologies. The integration of technologies such as the internet of things (IoT), cloud computing, and machine learning concepts have given rise to Industry 4.0. Fog and edge computing have stepped in to fill the areas where cloud computing is inadequate to ensure these systems work quickly and efficiently. The number of connected devices has brought about cybersecurity issues. This study reviewed the current literature regarding edge/fog-based cybersecurity in IoT to display the current state.

DOCUMENT

product

Realtime decision-making with 5G edge computing

At the 5Groningen field lab, the next generation of wireless technology is being put to the test in an experiment with a prototype involving real-time decision-making using 5G edge computing. One of the applications envisioned is a smart police vest that can fully automatically detect threats like firearms and stabbing weapons.

LINK

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Emerging Industrial Internet of Things Open-Source Platforms and Applications in Diverse Sectors

Revolutionary advances in technology have been seen in many industries, with the IIoT being a prime example. The IIoT creates a network of interconnected devices, allowing smooth communication and interoperability in industrial settings. This not only boosts efficiency, productivity, and safety but also provides transformative solutions for various sectors. This research looks into open-source IIoT and edge platforms that are applicable to a range of applications with the aim of finding and developing high-potential solutions. It highlights the effect of open-source IIoT and edge computing platforms on traditional IIoT applications, showing how these platforms make development and deployment processes easier. Popular open-source IIoT platforms include DeviceHive and Thingsboard, while EdgeX Foundry is a key platform for edge computing, allowing IIoT applications to be deployed closer to data sources, thus reducing latency and conserving bandwidth. This study seeks to identify potential future domains for the implementation of IIoT solutions using these open-source platforms. Additionally, each sector is evaluated based on various criteria, such as development requirement analyses, market demand projections, the examination of leading companies and emerging startups in each domain, and the application of the International Patent Classification (IPC) scheme for in-depth sector analysis.

MULTIFILE

Emerging Industrial Internet of Things Open-Source Platforms and Applications in Diverse Sectors

product

Optimization of an adverse outcome pathway network on chemical-induced cholestasis using an artificial intelligence-assisted data collection and confidence level quantification approach

Background: Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships. Methods: Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship. Results: This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis. Conclusions: This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.

DOCUMENT

Optimization of an adverse outcome pathway network on chemical-induced cholestasis using an artificial intelligence-assisted data collection and confidence level quantification approach

Projecten 7

project

European Digital Innovation Hub

The Northern Netherlands (NN) finds itself at the junction of all the big transitions. Digitalisation is essential to follow through with these. Considering this, our region has the potential to make sizeable progress if it can successfully roll out widespread digitalisation. As a hardcore transition economy, the NN may even join the European frontrunners and act as an example for other regions. It is from this challenge that the NN will start with the European Digital Innovation Hub (EDIH NN). We have chosen to specialise in the area of Autonomous Systems, which includes multiple digital technologies that are relevant for the four transitions in the NN: (1) Smart Agro, (2) Smart Manufacturing, (3) Life Science and Health and (4) Utilities, Built Environment and Mobility. In the first three-year EDIH NN wants to support more than 750 companies and lay the foundation for long-term support of all companies. The following building blocks for EDIH NN are: • A Brokerage network that will identify issues regarding digitalisation and relay these to Solution Providers (high TRL) and knowledge providers (low TRL). • A Test Before Invest network (test and demo facilities) comprising 20+ organisations that will invest in Autonomous Systems within their domain, and collaborate towards becoming a European testing ground. • A Smart Factory Accelerator to strengthen the digital maturity of companies. • An Empowerment programme to strengthen companies in the areas of DEP Technologies: Cyber Security and Artificial Intelligence. • An approach based on High Performance Computing to make digitalisation more accessible. • The Smart Makers Academy: A programme aimed at matching supply and demand around digital skills, based on individual learning outcomes. • A Funding Readiness programme to help companies that need to invest for their digitalisation strategy, in finding funding opportunities. • A network to stimulate supply and demand around Autonomous Systems

Lopend

project

LiDAR ten behoeve van Smart Asset Management in de publieke ruimte

Het is een tijds- en kostenintensief proces om de conditie van assets in de publieke ruimte te monitoren. Nieuwe technologie in de vorm van 3D LiDAR scanning biedt nieuwe mogelijkheden voor conditiemonitoring. Het doel van deze KIEM-aanvraag is (i) om de hardware geschikt te maken voor frequente en goedkope opnames in de stedelijke omgeving, (ii) de analysetechnieken van de geproduceerde datasets verder te ontwikkelen en (iii) een geannoteerde dataset gefocust op asset management te produceren. Dit zorgt ervoor dat publieke en MKB-partijen slimmere, snellere en volledigere onderhoudsbeslissingen kunnen nemen. Het consortium van Fietskoerier.nl, Sonarski, Gemeente Amsterdam en de Hogeschool van Amsterdam heeft elkaar gevonden in de vraag: “Hoe kan (publieke) LiDAR data bijdragen aan SMART Asset Management?” Dit project bevat een unieke combinatie van twee technologieën die op dit moment in ontwikkeling zijn (i) sensor data gedreven conditiemonitoring en (ii) point cloud algoritmes op LiDAR data. Fietskoerier.nl heeft de resources om op een duurzame manier de stad in kaart te brengen. Sonarski heeft een oplossing voor het uitvoeren van de 3D scans en Gemeente Amsterdam is een belangrijke kennispartner en heeft groot scala aan assets in de publieke ruimte. De deelnemers van dit project zien deze aanvraag als een eerste stap en hebben de intentie om te groeien tot een groter consortium welke de gehele keten van onderhoud omvat.

Afgerond

project

SEMI-REAL TIME MONITORING PROCESS DESIGN TOWARDS BIOCHEMICAL RECYCLING OF PLASTIC POLYMERS (POLYESTERS) WITH FUNGAL ENZYMES.

Plastic waste is one of the largest environmental problems in the 21st century. By 2050, up to 12,000 Mt of plastic waste is estimated to be in landfills or in the natural environment. Biochemical recycling by using modified microbial enzymes have shown potentials in the back-to-monomer (BTM) recycling of polyethylene terephthalate by breaking down the polymers into re-usable monomers. These enzymes can be produced via fungal species. In order to make this biochemical BTM process viable a process integrated enzyme production is key in increasing the efficiency and reducing the cost of enzymes. For this a molecular monitoring method, such as RNA-seq (RNA-sequencing), is needed. RNA-seq can achieve a snapshot on enzyme producing process inside of the cell by semi-quantitatively measuring the volume of enzyme encoding RNAs. This information can bring hints on fungal strain improvement by promoting the desired enzymes. It also helps to instantly monitor the BTM production outcomes. However, conventional RNA-seq platforms can only be performed via service providers or startup investments reaching 2 million euros. Each round of analysis could take as long as 6 weeks turnaround time. Furthermore, the method creates huge amount of complicated datasets, only by expert skills and specialized high performance computing the data can be sorted in a comprehensive manner. To solve these problems, in this project, by combining the expertise on plastic end-of-life control, fungal enzyme production, molecular monitoring and Bioinformatics from both the UAS and SME sides, we aim to implement a novel RNA-seq based system to monitor the in-process enzyme production for plastic degradation. We will optimize the existing portable RNA-seq prototype machinery for semi-real time monitoring of the BTM recycling process. The downstream data will be handled by a tailored analysis pipeline designed with expert knowledge via an user-friendly interface.

Afgerond

project

STEERS - towardS inTelligEnt intER-hub-logiStics

An efficient and sustainable logistics process is essential for logistics companies to remain competitive and to manage the dynamic demands and service requirements. Specifically, the first- and last-mile hub-to-hub (inter) logistics is one of the most difficult operations to manage due to low volumes, repetitive operation and short-distance transport, and relatively high waiting times. With the advancements in Industry 4.0 technologies (Internet of Things, Big Data, Cloud computing, Artificial Intelligence), the consortium partners expect that the intelligent and connected technology is a viable solution to improve operational efficiency, coordination, and sustainability of this inter-hub logistics. Despite the promising potential, the impact of technology on inter- and intra-hub (inside hub) logistics operations (such as transportation, communication, and planning) is not well-established. The focus of STEERS is to explore the real-life challenges associated with the logistics operation in a small-to-medium size logistics hub and investigate the potential of intelligent and connected technology to address such challenges. This project will investigate the requirements for the application of automated vehicles in inter-hub transportation and simultaneously explore the potential of intelligent inter-hub corridors. Additionally, inter-hub communications will also provide the opportunity to explore their potential impact on the planning and coordination of intra-hub activities, with an explicit focus on the changing role of human planners. It combines the knowledge of education and research institutes (Hogeschool van Arnhem en Nijmegen, The University of Twente and Hogeschool Rotterdam), logistics industry partners (Bolk Container Transport and Combi Terminal Twente) and public institutes (XL Business Park, Port of Twente and Regio Twente). The insights obtained in this exploratory study will serve as a foundation for the follow-up RAAK-PRO project, in which real-world demonstrators will be developed and tested inside XL Business Park.

Afgerond

project

Swarm collaborative Multi-Agent cybeR physical sysTems with shAred sensinG modalitiEs, 5G commuNication and micro-elecTromechanical Sensor arrays (SMART-AGENTS)

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.

Lopend

project

XAIPre: Explainable AI for Predictive Maintenance

Predictive maintenance, using data of thousands of sensors already available, is key for optimizing the maintenance schedule and further prevention of unexpected failures in industry.Current maintenance concepts (in the maritime industry) are based on a fixed maintenance interval for each piece of equipment with enough safety margin to minimize incidents. This means that maintenance is most of the time carried out too early and sometimes too late. This is in particular true for maintenance on maritime equipment, where onshore maintenance is strongly preferred over offshore maintenance and needs to be aligned with the vessel’s operations schedule. However, state-of-the-art predictive maintenance methods rely on black-box machine learning techniques such as deep neural networks that are difficult to interpret and are difficult to accept and work with for the maintenance engineers. The XAIPre project (pronounce Xyper) aims at developing Explainable Predictive Maintenance algorithms that do not only provide the engineers with a prediction but in addition, with a risk analysis on the components when delaying the maintenance, and what the primary indicators are that the algorithms use to create inference. To use predictive maintenance effectively in Maritime operations, the predictive models and also the optimization of the maintenance schedule using these models, need to be aware of the past and planned vessel activities, since different activities affect the lifetime of the machines differently. For example, the degradation of a hydraulic pump inside a crane depends on the type of operations the crane but also the vessel is performing. Thus the models do not only need to be explainable but they also need to be aware of the context which is in this case the vessel and machinery activity. Using sensor data processing and edge-computing technologies that will be developed and applied by the Hanze University of Applied Sciences in Groningen (Hanze UAS), context information is extracted from the raw sensor data. The XAIPre project combines these Explainable Context Aware Machine Learning models with state-of-the-art optimizers, that are already developed and available from the NWO CIMPLO project at LIACS, in order to develop optimal maintenance schedules for machine components. The resulting XAIPre prototype offers significant competitive advantages for maritime companies such as Heerema, by increasing the longevity of machine components, increasing worker safety and decreasing maintenance costs.

Lopend

project

XAIPre: Explainable AI for Predictive Maintenance

Predictive maintenance, using data of thousands of sensors already available, is key for optimizing the maintenance schedule and further prevention of unexpected failures in industry. Current maintenance concepts (in the maritime industry) are based on a fixed maintenance interval for each piece of equipment with enough safety margin to minimize incidents. This means that maintenance is most of the time carried out too early and sometimes too late. This is in particular true for maintenance on maritime equipment, where onshore maintenance is strongly preferred over offshore maintenance and needs to be aligned with the vessel’s operations schedule. However, state-of-the-art predictive maintenance methods rely on black-box machine learning techniques such as deep neural networks that are difficult to interpret and are difficult to accept and work with for the maintenance engineers. The XAIPre project (pronounce “Xyper”) aims at developing Explainable Predictive Maintenance (XPdM) algorithms that do not only provide the engineers with a prediction but in addition, with 1) a risk analysis on the components when delaying the maintenance, and 2) what the primary indicators are that the algorithms used to create inference. To use predictive maintenance effectively in Maritime operations, the predictive models and the optimization of the maintenance schedule using these models, need to be aware of the past and planned vessel activities, since different activities affect the lifetime of the machines differently. For example, the degradation of a hydraulic pump inside a crane depends on the type of operations the crane performs. Thus, the models do not only need to be explainable but they also need to be aware of the context which is in this case the vessel and machinery activity. Using sensor data processing and edge-computing technologies that will be developed and applied by the Hanze UAS in Groningen, context information is extracted from the raw sensor data. The XAIPre project combines these Explainable Context Aware Machine Learning models with state-of-the-art optimizers, that we already developed in the NWO CIMPLO project at LIACS, in order to develop optimal maintenance schedules for machine components. The optimizers will be adapted to fit within XAIPre. The resulting XAIPre prototype offers significant competitive advantages for companies such as Heerema, by increasing the longevity of machine components, increasing worker safety, and decreasing maintenance costs. XAIPre will focus on the predictive maintenance of thrusters, which is a key sub-system with regards to maintenance as it is a core part of the vessels station keeping capabilities. Periodic maintenance is currently required in fixed intervals of 5 years. XPdM can provide a solid base to deviate from the Periodic Maintenance prescriptions to reduce maintenance costs while maintaining quality. Scaling up to include additional components and systems after XAIPre will be relatively straightforward due to the accumulated knowledge of the predictive maintenance process and the delivered methods. Although the XAIPre system will be evaluated on the use-cases of Heerema, many components of the system can be utilized across industries to save maintenance costs, maximize worker safety and optimize sustainability.

Lopend

Producten 198

product

The role of Edge/Fog computing security in IoT and Industry 4.0 infrastructures

DOCUMENT

product

Realtime decision-making with 5G edge computing

LINK

product

Emerging Industrial Internet of Things Open-Source Platforms and Applications in Diverse Sectors

MULTIFILE

product

Optimization of an adverse outcome pathway network on chemical-induced cholestasis using an artificial intelligence-assisted data collection and confidence level quantification approach

DOCUMENT

Projecten 7

project

European Digital Innovation Hub

Lopend

project

LiDAR ten behoeve van Smart Asset Management in de publieke ruimte

Afgerond

project

SEMI-REAL TIME MONITORING PROCESS DESIGN TOWARDS BIOCHEMICAL RECYCLING OF PLASTIC POLYMERS (POLYESTERS) WITH FUNGAL ENZYMES.

Afgerond

project

STEERS - towardS inTelligEnt intER-hub-logiStics

Afgerond

project

Swarm collaborative Multi-Agent cybeR physical sysTems with shAred sensinG modalitiEs, 5G commuNication and micro-elecTromechanical Sensor arrays (SMART-AGENTS)

Lopend

project

XAIPre: Explainable AI for Predictive Maintenance

Lopend

project

XAIPre: Explainable AI for Predictive Maintenance

Predictive maintenance, using data of thousands of sensors already available, is key for optimizing the maintenance schedule and further prevention of unexpected failures in industry. Current maintenance concepts (in the maritime industry) are based on a fixed maintenance interval for each piece of equipment with enough safety margin to minimize incidents. This means that maintenance is most of the time carried out too early and sometimes too late. This is in particular true for maintenance on maritime equipment, where onshore maintenance is strongly preferred over offshore maintenance and needs to be aligned with the vessel’s operations schedule. However, state-of-the-art predictive maintenance methods rely on black-box machine learning techniques such as deep neural networks that are difficult to interpret and are difficult to accept and work with for the maintenance engineers. The XAIPre project (pronounce “Xyper”) aims at developing Explainable Predictive Maintenance (XPdM) algorithms that do not only provide the engineers with a prediction but in addition, with 1) a risk analysis on the components when delaying the maintenance, and 2) what the primary indicators are that the algorithms used to create inference. To use predictive maintenance effectively in Maritime operations, the predictive models and the optimization of the maintenance schedule using these models, need to be aware of the past and planned vessel activities, since different activities affect the lifetime of the machines differently. For example, the degradation of a hydraulic pump inside a crane depends on the type of operations the crane performs. Thus, the models do not only need to be explainable but they also need to be aware of the context which is in this case the vessel and machinery activity. Using sensor data processing and edge-computing technologies that will be developed and applied by the Hanze UAS in Groningen, context information is extracted from the raw sensor data. The XAIPre project combines these Explainable Context Aware Machine Learning models with state-of-the-art optimizers, that we already developed in the NWO CIMPLO project at LIACS, in order to develop optimal maintenance schedules for machine components. The optimizers will be adapted to fit within XAIPre. The resulting XAIPre prototype offers significant competitive advantages for companies such as Heerema, by increasing the longevity of machine components, increasing worker safety, and decreasing maintenance costs. XAIPre will focus on the predictive maintenance of thrusters, which is a key sub-system with regards to maintenance as it is a core part of the vessels station keeping capabilities. Periodic maintenance is currently required in fixed intervals of 5 years. XPdM can provide a solid base to deviate from the Periodic Maintenance prescriptions to reduce maintenance costs while maintaining quality. Scaling up to include additional components and systems after XAIPre will be relatively straightforward due to the accumulated knowledge of the predictive maintenance process and the delivered methods. Although the XAIPre system will be evaluated on the use-cases of Heerema, many components of the system can be utilized across industries to save maintenance costs, maximize worker safety and optimize sustainability.

Lopend

Zoekresultaten

Producten 198

The role of Edge/Fog computing security in IoT and Industry 4.0 infrastructures

Realtime decision-making with 5G edge computing

Emerging Industrial Internet of Things Open-Source Platforms and Applications in Diverse Sectors

Inclusive Dialogues: WokeBot Engaging Diversity Dilemmas

Optimization of an adverse outcome pathway network on chemical-induced cholestasis using an artificial intelligence-assisted data collection and confidence level quantification approach

I’M A.I. IMage Processing and Artificial Intelligence [Inaugurele rede]

Harmonizing and improving European education in prescribing

Object Recognition by Contour Matching

Motion Control with Vision

Projecten 7

European Digital Innovation Hub

LiDAR ten behoeve van Smart Asset Management in de publieke ruimte

SEMI-REAL TIME MONITORING PROCESS DESIGN TOWARDS BIOCHEMICAL RECYCLING OF PLASTIC POLYMERS (POLYESTERS) WITH FUNGAL ENZYMES.

STEERS - towardS inTelligEnt intER-hub-logiStics

Swarm collaborative Multi-Agent cybeR physical sysTems with shAred sensinG modalitiEs, 5G commuNication and micro-elecTromechanical Sensor arrays (SMART-AGENTS)

XAIPre: Explainable AI for Predictive Maintenance

XAIPre: Explainable AI for Predictive Maintenance

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Producten 198

The role of Edge/Fog computing security in IoT and Industry 4.0 infrastructures

Realtime decision-making with 5G edge computing

Emerging Industrial Internet of Things Open-Source Platforms and Applications in Diverse Sectors

Inclusive Dialogues: WokeBot Engaging Diversity Dilemmas

Optimization of an adverse outcome pathway network on chemical-induced cholestasis using an artificial intelligence-assisted data collection and confidence level quantification approach

I’M A.I. IMage Processing and Artificial Intelligence [Inaugurele rede]

Harmonizing and improving European education in prescribing

Object Recognition by Contour Matching

Motion Control with Vision

Projecten 7

European Digital Innovation Hub

LiDAR ten behoeve van Smart Asset Management in de publieke ruimte

SEMI-REAL TIME MONITORING PROCESS DESIGN TOWARDS BIOCHEMICAL RECYCLING OF PLASTIC POLYMERS (POLYESTERS) WITH FUNGAL ENZYMES.

STEERS - towardS inTelligEnt intER-hub-logiStics

Swarm collaborative Multi-Agent cybeR physical sysTems with shAred sensinG modalitiEs, 5G commuNication and micro-elecTromechanical Sensor arrays (SMART-AGENTS)

XAIPre: Explainable AI for Predictive Maintenance

XAIPre: Explainable AI for Predictive Maintenance