2,907 research outputs found
Deep generative models for network data synthesis and monitoring
Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network.
Although networks inherently
have abundant amounts of monitoring data, its access and effective measurement is
another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset
without leaking commercial sensitive information. Second, it could be very expensive
to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of
flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources
in the network element that can be applied to support the measurement function are
too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex
structure. Various emerging optimization-based solutions (e.g., compressive sensing)
or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet
meet the current network requirements.
The contributions made in this thesis significantly advance the state of the art in
the domain of network measurement and monitoring techniques. Overall, we leverage
cutting-edge machine learning technology, deep generative modeling, throughout the
entire thesis. First, we design and realize APPSHOT , an efficient city-scale network
traffic sharing with a conditional generative model, which only requires open-source
contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system β GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we
design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time
network telemetry system with latent GANs and spectral-temporal networks. Finally,
we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through
this research are summarized, and interesting topics are discussed for future work in
this domain. All proposed solutions have been evaluated with real-world datasets and
applied to support different applications in real systems
Configuration Management of Distributed Systems over Unreliable and Hostile Networks
Economic incentives of large criminal profits and the threat of legal consequences have pushed criminals to continuously improve their malware, especially command and control channels. This thesis applied concepts from successful malware command and control to explore the survivability and resilience of benign configuration management systems.
This work expands on existing stage models of malware life cycle to contribute a new model for identifying malware concepts applicable to benign configuration management. The Hidden Master architecture is a contribution to master-agent network communication. In the Hidden Master architecture, communication between master and agent is asynchronous and can operate trough intermediate nodes. This protects the master secret key, which gives full control of all computers participating in configuration management. Multiple improvements to idempotent configuration were proposed, including the definition of the minimal base resource dependency model, simplified resource revalidation and the use of imperative general purpose language for defining idempotent configuration.
Following the constructive research approach, the improvements to configuration management were designed into two prototypes. This allowed validation in laboratory testing, in two case studies and in expert interviews. In laboratory testing, the Hidden Master prototype was more resilient than leading configuration management tools in high load and low memory conditions, and against packet loss and corruption. Only the research prototype was adaptable to a network without stable topology due to the asynchronous nature of the Hidden Master architecture.
The main case study used the research prototype in a complex environment to deploy a multi-room, authenticated audiovisual system for a client of an organization deploying the configuration. The case studies indicated that imperative general purpose language can be used for idempotent configuration in real life, for defining new configurations in unexpected situations using the base resources, and abstracting those using standard language features; and that such a system seems easy to learn.
Potential business benefits were identified and evaluated using individual semistructured expert interviews. Respondents agreed that the models and the Hidden Master architecture could reduce costs and risks, improve developer productivity and allow faster time-to-market. Protection of master secret keys and the reduced need for incident response were seen as key drivers for improved security. Low-cost geographic scaling and leveraging file serving capabilities of commodity servers were seen to improve scaling and resiliency. Respondents identified jurisdictional legal limitations to encryption and requirements for cloud operator auditing as factors potentially limiting the full use of some concepts
Pristup specifikaciji i generisanju proizvodnih procesa zasnovan na inΕΎenjerstvu voΔenom modelima
In this thesis, we present an approach to the production process specification and generation based on the model-driven paradigm, with the goal to increase the flexibility of factories and respond to the challenges that emerged in the era of Industry 4.0 more efficiently. To formally specify production processes and their variations in the Industry 4.0 environment, we created a novel domain-specific modeling language, whose models are machine-readable. The created language can be used to model production processes that can be independent of any production system, enabling process models to be used in different production systems, and process models used for the specific production system. To automatically transform production process models dependent on the specific production system into instructions that are to be executed by production system resources, we created an instruction generator. Also, we created generators for different manufacturing documentation, which automatically transform production process models into manufacturing documents of different types. The proposed approach, domain-specific modeling language, and software solution contribute to introducing factories into the digital transformation process. As factories must rapidly adapt to new products and their variations in the era of Industry 4.0, production must be dynamically led and instructions must be automatically sent to factory resources, depending on products that are to be created on the shop floor. The proposed approach contributes to the creation of such a dynamic environment in contemporary factories, as it allows to automatically generate instructions from process models and send them to resources for execution. Additionally, as there are numerous different products and their variations, keeping the required manufacturing documentation up to date becomes challenging, which can be done automatically by using the proposed approach and thus significantly lower process designers' time.Π£ ΠΎΠ²ΠΎΡ Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠΈ ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΡΠ΅Π½ ΡΠ΅ ΠΏΡΠΈΡΡΡΠΏ ΡΠΏΠ΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠΈ ΠΈ Π³Π΅Π½Π΅ΡΠΈΡΠ°ΡΡ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΠ° Π·Π°ΡΠ½ΠΎΠ²Π°Π½ Π½Π° ΠΈΠ½ΠΆΠ΅ΡΠ΅ΡΡΡΠ²Ρ Π²ΠΎΡΠ΅Π½ΠΎΠΌ ΠΌΠΎΠ΄Π΅Π»ΠΈΠΌΠ°, Ρ ΡΠΈΡΡ ΠΏΠΎΠ²Π΅ΡΠ°ΡΠ° ΡΠ»Π΅ΠΊΡΠΈΠ±ΠΈΠ»Π½ΠΎΡΡΠΈ ΠΏΠΎΡΡΡΠΎΡΠ΅ΡΠ° Ρ ΡΠ°Π±ΡΠΈΠΊΠ°ΠΌΠ° ΠΈ Π΅ΡΠΈΠΊΠ°ΡΠ½ΠΈΡΠ΅Π³ ΡΠ°Π·ΡΠ΅ΡΠ°Π²Π°ΡΠ° ΠΈΠ·Π°Π·ΠΎΠ²Π° ΠΊΠΎΡΠΈ ΡΠ΅ ΠΏΠΎΡΠ°Π²ΡΡΡΡ Ρ Π΅ΡΠΈ ΠΠ½Π΄ΡΡΡΡΠΈΡΠ΅ 4.0. ΠΠ° ΠΏΠΎΡΡΠ΅Π±Π΅ ΡΠΎΡΠΌΠ°Π»Π½Π΅ ΡΠΏΠ΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠ΅ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΠ° ΠΈ ΡΠΈΡ
ΠΎΠ²ΠΈΡ
Π²Π°ΡΠΈΡΠ°ΡΠΈΡΠ° Ρ Π°ΠΌΠ±ΠΈΡΠ΅Π½ΡΡ ΠΠ½Π΄ΡΡΡΡΠΈΡΠ΅ 4.0, ΠΊΡΠ΅ΠΈΡΠ°Π½ ΡΠ΅ Π½ΠΎΠ²ΠΈ Π½Π°ΠΌΠ΅Π½ΡΠΊΠΈ ΡΠ΅Π·ΠΈΠΊ, ΡΠΈΡΠ΅ ΠΌΠΎΠ΄Π΅Π»Π΅ ΡΠ°ΡΡΠ½Π°Ρ ΠΌΠΎΠΆΠ΅ Π΄Π° ΠΎΠ±ΡΠ°Π΄ΠΈ Π½Π° Π°ΡΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°ΡΠΈΠ½. ΠΡΠ΅ΠΈΡΠ°Π½ΠΈ ΡΠ΅Π·ΠΈΠΊ ΠΈΠΌΠ° ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡ ΠΌΠΎΠ΄Π΅Π»ΠΎΠ²Π°ΡΠ° ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΠ° ΠΊΠΎΡΠΈ ΠΌΠΎΠ³Ρ Π±ΠΈΡΠΈ Π½Π΅Π·Π°Π²ΠΈΡΠ½ΠΈ ΠΎΠ΄ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ° ΠΈ ΡΠΈΠΌΠ΅ ΡΠΏΠΎΡΡΠ΅Π±ΡΠ΅Π½ΠΈ Ρ ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΠΌ ΠΏΠΎΡΡΡΠΎΡΠ΅ΡΠΈΠΌΠ° ΠΈΠ»ΠΈ ΡΠ°Π±ΡΠΈΠΊΠ°ΠΌΠ°, Π°Π»ΠΈ ΠΈ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΠ° ΠΊΠΎΡΠΈ ΡΡ ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ½ΠΈ Π·Π° ΠΎΠ΄ΡΠ΅ΡΠ΅Π½ΠΈ ΡΠΈΡΡΠ΅ΠΌ. ΠΠ°ΠΊΠΎ Π±ΠΈ ΠΌΠΎΠ΄Π΅Π»Π΅ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΠ° Π·Π°Π²ΠΈΡΠ½ΠΈΡ
ΠΎΠ΄ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠ³ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΎΠ³ ΡΠΈΡΡΠ΅ΠΌΠ° Π±ΠΈΠ»ΠΎ ΠΌΠΎΠ³ΡΡΠ΅ Π½Π° Π°ΡΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°ΡΠΈΠ½ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠΈΡΠ°ΡΠΈ Ρ ΠΈΠ½ΡΡΡΡΠΊΡΠΈΡΠ΅ ΠΊΠΎΡΠ΅ ΡΠ΅ΡΡΡΡΠΈ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΎΠ³ ΡΠΈΡΡΠ΅ΠΌΠ° ΠΈΠ·Π²ΡΡΠ°Π²Π°ΡΡ, ΠΊΡΠ΅ΠΈΡΠ°Π½ ΡΠ΅ Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡ ΠΈΠ½ΡΡΡΡΠΊΡΠΈΡΠ°. Π’Π°ΠΊΠΎΡΠ΅ ΡΡ ΠΊΡΠ΅ΠΈΡΠ°Π½ΠΈ ΠΈ Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡΠΈ ΡΠ΅Ρ
Π½ΠΈΡΠΊΠ΅ Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ°ΡΠΈΡΠ΅, ΠΊΠΎΡΠΈ Π½Π° Π°ΡΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°ΡΠΈΠ½ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠΈΡΡ ΠΌΠΎΠ΄Π΅Π»Π΅ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΠ° Ρ Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ΅ ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΡ
ΡΠΈΠΏΠΎΠ²Π°. Π£ΠΏΠΎΡΡΠ΅Π±ΠΎΠΌ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎΠ³ ΠΏΡΠΈΡΡΡΠΏΠ°, Π½Π°ΠΌΠ΅Π½ΡΠΊΠΎΠ³ ΡΠ΅Π·ΠΈΠΊΠ° ΠΈ ΡΠΎΡΡΠ²Π΅ΡΡΠΊΠΎΠ³ ΡΠ΅ΡΠ΅ΡΠ° Π΄ΠΎΠΏΡΠΈΠ½ΠΎΡΠΈ ΡΠ΅ ΡΠ²ΠΎΡΠ΅ΡΡ ΡΠ°Π±ΡΠΈΠΊΠ° Ρ ΠΏΡΠΎΡΠ΅Ρ Π΄ΠΈΠ³ΠΈΡΠ°Π»Π½Π΅ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΠΈΡΠ΅. ΠΠ°ΠΊΠΎ ΡΠ°Π±ΡΠΈΠΊΠ΅ Ρ Π΅ΡΠΈ ΠΠ½Π΄ΡΡΡΡΠΈΡΠ΅ 4.0 ΠΌΠΎΡΠ°ΡΡ Π±ΡΠ·ΠΎ Π΄Π° ΡΠ΅ ΠΏΡΠΈΠ»Π°Π³ΠΎΠ΄Π΅ Π½ΠΎΠ²ΠΈΠΌ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠΌΠ° ΠΈ ΡΠΈΡ
ΠΎΠ²ΠΈΠΌ Π²Π°ΡΠΈΡΠ°ΡΠΈΡΠ°ΠΌΠ°, Π½Π΅ΠΎΠΏΡ
ΠΎΠ΄Π½ΠΎ ΡΠ΅ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠΊΠΈ Π²ΠΎΠ΄ΠΈΡΠΈ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡ ΠΈ Π½Π° Π°ΡΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°ΡΠΈΠ½ ΡΠ»Π°ΡΠΈ ΠΈΠ½ΡΡΡΡΠΊΡΠΈΡΠ΅ ΡΠ΅ΡΡΡΡΠΈΠΌΠ° Ρ ΡΠ°Π±ΡΠΈΡΠΈ, Ρ Π·Π°Π²ΠΈΡΠ½ΠΎΡΡΠΈ ΠΎΠ΄ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π° ΠΊΠΎΡΠΈ ΡΠ΅ ΠΊΡΠ΅ΠΈΡΠ°ΡΡ Ρ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠΌ ΠΏΠΎΡΡΡΠΎΡΠ΅ΡΡ. Π’ΠΈΠΌΠ΅ ΡΡΠΎ ΡΠ΅ Ρ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎΠΌ ΠΏΡΠΈΡΡΡΠΏΡ ΠΌΠΎΠ³ΡΡΠ΅ ΠΈΠ· ΠΌΠΎΠ΄Π΅Π»Π° ΠΏΡΠΎΡΠ΅ΡΠ° Π°ΡΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ΠΎ Π³Π΅Π½Π΅ΡΠΈΡΠ°ΡΠΈ ΠΈΠ½ΡΡΡΡΠΊΡΠΈΡΠ΅ ΠΈ ΠΏΠΎΡΠ»Π°ΡΠΈ ΠΈΡ
ΡΠ΅ΡΡΡΡΠΈΠΌΠ°, Π΄ΠΎΠΏΡΠΈΠ½ΠΎΡΠΈ ΡΠ΅ ΠΊΡΠ΅ΠΈΡΠ°ΡΡ ΡΠ΅Π΄Π½ΠΎΠ³ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠΊΠΎΠ³ ΠΎΠΊΡΡΠΆΠ΅ΡΠ° Ρ ΡΠ°Π²ΡΠ΅ΠΌΠ΅Π½ΠΈΠΌ ΡΠ°Π±ΡΠΈΠΊΠ°ΠΌΠ°. ΠΠΎΠ΄Π°ΡΠ½ΠΎ, ΡΡΠ»Π΅Π΄ Π²Π΅Π»ΠΈΠΊΠΎΠ³ Π±ΡΠΎΡΠ° ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΡ
ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π° ΠΈ ΡΠΈΡ
ΠΎΠ²ΠΈΡ
Π²Π°ΡΠΈΡΠ°ΡΠΈΡΠ°, ΠΏΠΎΡΡΠ°ΡΠ΅ ΠΈΠ·Π°Π·ΠΎΠ²Π½ΠΎ ΠΎΠ΄ΡΠΆΠ°Π²Π°ΡΠΈ Π½Π΅ΠΎΠΏΡ
ΠΎΠ΄Π½Ρ ΡΠ΅Ρ
Π½ΠΈΡΠΊΡ Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ°ΡΠΈΡΡ, ΡΡΠΎ ΡΠ΅ Ρ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎΠΌ ΠΏΡΠΈΡΡΡΠΏΡ ΠΌΠΎΠ³ΡΡΠ΅ ΡΡΠ°Π΄ΠΈΡΠΈ Π½Π° Π°ΡΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°ΡΠΈΠ½ ΠΈ ΡΠΈΠΌΠ΅ Π·Π½Π°ΡΠ°ΡΠ½ΠΎ ΡΡΡΠ΅Π΄Π΅ΡΠΈ Π²ΡΠ΅ΠΌΠ΅ ΠΏΡΠΎΡΠ΅ΠΊΡΠ°Π½Π°ΡΠ° ΠΏΡΠΎΡΠ΅ΡΠ°.U ovoj disertaciji predstavljen je pristup specifikaciji i generisanju proizvodnih procesa zasnovan na inΕΎenjerstvu voΔenom modelima, u cilju poveΔanja fleksibilnosti postrojenja u fabrikama i efikasnijeg razreΕ‘avanja izazova koji se pojavljuju u eri Industrije 4.0. Za potrebe formalne specifikacije proizvodnih procesa i njihovih varijacija u ambijentu Industrije 4.0, kreiran je novi namenski jezik, Δije modele raΔunar moΕΎe da obradi na automatizovan naΔin. Kreirani jezik ima moguΔnost modelovanja proizvodnih procesa koji mogu biti nezavisni od proizvodnih sistema i time upotrebljeni u razliΔitim postrojenjima ili fabrikama, ali i proizvodnih procesa koji su specifiΔni za odreΔeni sistem. Kako bi modele proizvodnih procesa zavisnih od konkretnog proizvodnog sistema bilo moguΔe na automatizovan naΔin transformisati u instrukcije koje resursi proizvodnog sistema izvrΕ‘avaju, kreiran je generator instrukcija. TakoΔe su kreirani i generatori tehniΔke dokumentacije, koji na automatizovan naΔin transformiΕ‘u modele proizvodnih procesa u dokumente razliΔitih tipova. Upotrebom predloΕΎenog pristupa, namenskog jezika i softverskog reΕ‘enja doprinosi se uvoΔenju fabrika u proces digitalne transformacije. Kako fabrike u eri Industrije 4.0 moraju brzo da se prilagode novim proizvodima i njihovim varijacijama, neophodno je dinamiΔki voditi proizvodnju i na automatizovan naΔin slati instrukcije resursima u fabrici, u zavisnosti od proizvoda koji se kreiraju u konkretnom postrojenju. Time Ε‘to je u predloΕΎenom pristupu moguΔe iz modela procesa automatizovano generisati instrukcije i poslati ih resursima, doprinosi se kreiranju jednog dinamiΔkog okruΕΎenja u savremenim fabrikama. Dodatno, usled velikog broja razliΔitih proizvoda i njihovih varijacija, postaje izazovno odrΕΎavati neophodnu tehniΔku dokumentaciju, Ε‘to je u predloΕΎenom pristupu moguΔe uraditi na automatizovan naΔin i time znaΔajno uΕ‘tedeti vreme projektanata procesa
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (βAIβ) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics β and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the CatΓ³lica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
2023-2024 Catalog
The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation
2017 GREAT Day Program
SUNY Geneseoβs Eleventh Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1011/thumbnail.jp
Systemic Circular Economy Solutions for Fiber Reinforced Composites
This open access book provides an overview of the work undertaken within the FiberEUse project, which developed solutions enhancing the profitability of composite recycling and reuse in value-added products, with a cross-sectorial approach. Glass and carbon fiber reinforced polymers, or composites, are increasingly used as structural materials in many manufacturing sectors like transport, constructions and energy due to their better lightweight and corrosion resistance compared to metals. However, composite recycling is still a challenge since no significant added value in the recycling and reprocessing of composites is demonstrated. FiberEUse developed innovative solutions and business models towards sustainable Circular Economy solutions for post-use composite-made products. Three strategies are presented, namely mechanical recycling of short fibers, thermal recycling of long fibers and modular car parts design for sustainable disassembly and remanufacturing. The validation of the FiberEUse approach within eight industrial demonstrators shows the potentials towards new Circular Economy value-chains for composite materials
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