17,327 research outputs found
Key technologies for safe and autonomous drones
Drones/UAVs are able to perform air operations that are very difficult to be performed by manned aircrafts. In addition, drones' usage brings significant economic savings and environmental benefits, while reducing risks to human life. In this paper, we present key technologies that enable development of drone systems. The technologies are identified based on the usages of drones (driven by COMP4DRONES project use cases). These technologies are grouped into four categories: U-space capabilities, system functions, payloads, and tools. Also, we present the contributions of the COMP4DRONES project to improve existing technologies. These contributions aim to ease drones’ customization, and enable their safe operation.This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 826610. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Austria, Belgium, Czech Republic, France, Italy, Latvia, Netherlands. The total project budget is 28,590,748.75 EUR (excluding ESIF partners), while the requested grant is 7,983,731.61 EUR to ECSEL JU, and 8,874,523.84 EUR of National and ESIF Funding. The project has been started on 1st October 2019
Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions
In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request
Optimizations of Autoencoders for Analysis and Classification of Microscopic In Situ Hybridization Images
Currently, analysis of microscopic In Situ Hybridization images is done
manually by experts. Precise evaluation and classification of such microscopic
images can ease experts' work and reveal further insights about the data. In
this work, we propose a deep-learning framework to detect and classify areas of
microscopic images with similar levels of gene expression. The data we analyze
requires an unsupervised learning model for which we employ a type of
Artificial Neural Network - Deep Learning Autoencoders. The model's performance
is optimized by balancing the latent layers' length and complexity and
fine-tuning hyperparameters. The results are validated by adapting the
mean-squared error (MSE) metric, and comparison to expert's evaluation.Comment: 9 pages; 9 figure
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference
We propose Conditional Adapter (CoDA), a parameter-efficient transfer
learning method that also improves inference efficiency. CoDA generalizes
beyond standard adapter approaches to enable a new way of balancing speed and
accuracy using conditional computation. Starting with an existing dense
pretrained model, CoDA adds sparse activation together with a small number of
new parameters and a light-weight training phase. Our experiments demonstrate
that the CoDA approach provides an unexpectedly efficient way to transfer
knowledge. Across a variety of language, vision, and speech tasks, CoDA
achieves a 2x to 8x inference speed-up compared to the state-of-the-art Adapter
approach with moderate to no accuracy loss and the same parameter efficiency
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era
OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is
demonstrated to be one small step for generative AI (GAI), but one giant leap
for artificial general intelligence (AGI). Since its official release in
November 2022, ChatGPT has quickly attracted numerous users with extensive
media coverage. Such unprecedented attention has also motivated numerous
researchers to investigate ChatGPT from various aspects. According to Google
scholar, there are more than 500 articles with ChatGPT in their titles or
mentioning it in their abstracts. Considering this, a review is urgently
needed, and our work fills this gap. Overall, this work is the first to survey
ChatGPT with a comprehensive review of its underlying technology, applications,
and challenges. Moreover, we present an outlook on how ChatGPT might evolve to
realize general-purpose AIGC (a.k.a. AI-generated content), which will be a
significant milestone for the development of AGI.Comment: A Survey on ChatGPT and GPT-4, 29 pages. Feedback is appreciated
([email protected]
Exploiting Symmetry and Heuristic Demonstrations in Off-policy Reinforcement Learning for Robotic Manipulation
Reinforcement learning demonstrates significant potential in automatically
building control policies in numerous domains, but shows low efficiency when
applied to robot manipulation tasks due to the curse of dimensionality. To
facilitate the learning of such tasks, prior knowledge or heuristics that
incorporate inherent simplification can effectively improve the learning
performance. This paper aims to define and incorporate the natural symmetry
present in physical robotic environments. Then, sample-efficient policies are
trained by exploiting the expert demonstrations in symmetrical environments
through an amalgamation of reinforcement and behavior cloning, which gives the
off-policy learning process a diverse yet compact initiation. Furthermore, it
presents a rigorous framework for a recent concept and explores its scope for
robot manipulation tasks. The proposed method is validated via two
point-to-point reaching tasks of an industrial arm, with and without an
obstacle, in a simulation experiment study. A PID controller, which tracks the
linear joint-space trajectories with hard-coded temporal logic to produce
interim midpoints, is used to generate demonstrations in the study. The results
of the study present the effect of the number of demonstrations and quantify
the magnitude of behavior cloning to exemplify the possible improvement of
model-free reinforcement learning in common manipulation tasks. A comparison
study between the proposed method and a traditional off-policy reinforcement
learning algorithm indicates its advantage in learning performance and
potential value for applications
Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective
This paper introduces a comprehensive, multi-stage machine learning
methodology that effectively integrates information systems and artificial
intelligence to enhance decision-making processes within the domain of
operations research. The proposed framework adeptly addresses common
limitations of existing solutions, such as the neglect of data-driven
estimation for vital production parameters, exclusive generation of point
forecasts without considering model uncertainty, and lacking explanations
regarding the sources of such uncertainty. Our approach employs Quantile
Regression Forests for generating interval predictions, alongside both local
and global variants of SHapley Additive Explanations for the examined
predictive process monitoring problem. The practical applicability of the
proposed methodology is substantiated through a real-world production planning
case study, emphasizing the potential of prescriptive analytics in refining
decision-making procedures. This paper accentuates the imperative of addressing
these challenges to fully harness the extensive and rich data resources
accessible for well-informed decision-making
UniverSeg: Universal Medical Image Segmentation
While deep learning models have become the predominant method for medical
image segmentation, they are typically not capable of generalizing to unseen
segmentation tasks involving new anatomies, image modalities, or labels. Given
a new segmentation task, researchers generally have to train or fine-tune
models, which is time-consuming and poses a substantial barrier for clinical
researchers, who often lack the resources and expertise to train neural
networks. We present UniverSeg, a method for solving unseen medical
segmentation tasks without additional training. Given a query image and example
set of image-label pairs that define a new segmentation task, UniverSeg employs
a new Cross-Block mechanism to produce accurate segmentation maps without the
need for additional training. To achieve generalization to new tasks, we have
gathered and standardized a collection of 53 open-access medical segmentation
datasets with over 22,000 scans, which we refer to as MegaMedical. We used this
collection to train UniverSeg on a diverse set of anatomies and imaging
modalities. We demonstrate that UniverSeg substantially outperforms several
related methods on unseen tasks, and thoroughly analyze and draw insights about
important aspects of the proposed system. The UniverSeg source code and model
weights are freely available at https://universeg.csail.mit.eduComment: Victor and Jose Javier contributed equally to this work. Project
Website: https://universeg.csail.mit.ed
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness.
A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense.
Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice
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