23,436 research outputs found
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
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
An exploration of the language within Ofsted reports and their influence on primary school performance in mathematics: a mixed methods critical discourse analysis
This thesis contributes to the understanding of the language of Ofsted reports, their similarity to one another and associations between different terms used within ‘areas for improvement’ sections and subsequent outcomes for pupils. The research responds to concerns from serving headteachers that Ofsted reports are overly similar, do not capture the unique story of their school, and are unhelpful for improvement. In seeking to answer ‘how similar are
Ofsted reports’ the study uses two tools, a plagiarism detection software (Turnitin) and a discourse analysis tool (NVivo) to identify trends within and across a large corpus of reports.
The approach is based on critical discourse analysis (Van Dijk, 2009; Fairclough, 1989) but shaped in the form of practitioner enquiry seeking power in the form of impact on pupils and practitioners, rather than a more traditional, sociological application of the method.
The research found that in 2017, primary school section 5 Ofsted reports had more than half of their content exactly duplicated within other primary school inspection reports published that same year. Discourse analysis showed the quality assurance process overrode variables such as inspector designation, gender, or team size, leading to three distinct patterns of duplication: block duplication, self-referencing, and template writing. The most unique part of a report was found to be the ‘area for improvement’ section, which was tracked to externally verified outcomes for pupils using terms linked to ‘mathematics’. Those
required to improve mathematics in their areas for improvement improved progress and attainment in mathematics significantly more than national rates. These findings indicate that there was a positive correlation between the inspection reporting process and a beneficial impact on pupil outcomes in mathematics, and that the significant similarity of one report to another had no bearing on the usefulness of the report for school improvement purposes
within this corpus
The Viability and Potential Consequences of IoT-Based Ransomware
With the increased threat of ransomware and the substantial growth of the Internet of Things (IoT) market, there is significant motivation for attackers to carry out IoT-based ransomware campaigns. In this thesis, the viability of such malware is tested.
As part of this work, various techniques that could be used by ransomware developers to attack commercial IoT devices were explored. First, methods that attackers could use to communicate with the victim were examined, such that a ransom note was able to be reliably sent to a victim. Next, the viability of using "bricking" as a method of ransom was evaluated, such that devices could be remotely disabled unless the victim makes a payment to the attacker. Research was then performed to ascertain whether it was possible to remotely gain persistence on IoT devices, which would improve the efficacy of existing ransomware methods, and provide opportunities for more advanced ransomware to be created. Finally, after successfully identifying a number of persistence techniques, the viability of privacy-invasion based ransomware was analysed.
For each assessed technique, proofs of concept were developed. A range of devices -- with various intended purposes, such as routers, cameras and phones -- were used to test the viability of these proofs of concept. To test communication hijacking, devices' "channels of communication" -- such as web services and embedded screens -- were identified, then hijacked to display custom ransom notes. During the analysis of bricking-based ransomware, a working proof of concept was created, which was then able to remotely brick five IoT devices. After analysing the storage design of an assortment of IoT devices, six different persistence techniques were identified, which were then successfully tested on four devices, such that malicious filesystem modifications would be retained after the device was rebooted. When researching privacy-invasion based ransomware, several methods were created to extract information from data sources that can be commonly found on IoT devices, such as nearby WiFi signals, images from cameras, or audio from microphones. These were successfully implemented in a test environment such that ransomable data could be extracted, processed, and stored for later use to blackmail the victim.
Overall, IoT-based ransomware has not only been shown to be viable but also highly damaging to both IoT devices and their users. While the use of IoT-ransomware is still very uncommon "in the wild", the techniques demonstrated within this work highlight an urgent need to improve the security of IoT devices to avoid the risk of IoT-based ransomware causing havoc in our society. Finally, during the development of these proofs of concept, a number of potential countermeasures were identified, which can be used to limit the effectiveness of the attacking techniques discovered in this PhD research
Reinforcement Learning from Passive Data via Latent Intentions
Passive observational data, such as human videos, is abundant and rich in
information, yet remains largely untapped by current RL methods. Perhaps
surprisingly, we show that passive data, despite not having reward or action
labels, can still be used to learn features that accelerate downstream RL. Our
approach learns from passive data by modeling intentions: measuring how the
likelihood of future outcomes change when the agent acts to achieve a
particular task. We propose a temporal difference learning objective to learn
about intentions, resulting in an algorithm similar to conventional RL, but
which learns entirely from passive data. When optimizing this objective, our
agent simultaneously learns representations of states, of policies, and of
possible outcomes in an environment, all from raw observational data. Both
theoretically and empirically, this scheme learns features amenable for value
prediction for downstream tasks, and our experiments demonstrate the ability to
learn from many forms of passive data, including cross-embodiment video data
and YouTube videos.Comment: Accompanying website at https://dibyaghosh.com/icvf
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]
Fair Grading Algorithms for Randomized Exams
This paper studies grading algorithms for randomized exams. In a randomized
exam, each student is asked a small number of random questions from a large
question bank. The predominant grading rule is simple averaging, i.e.,
calculating grades by averaging scores on the questions each student is asked,
which is fair ex-ante, over the randomized questions, but not fair ex-post, on
the realized questions. The fair grading problem is to estimate the average
grade of each student on the full question bank. The maximum-likelihood
estimator for the Bradley-Terry-Luce model on the bipartite student-question
graph is shown to be consistent with high probability when the number of
questions asked to each student is at least the cubed-logarithm of the number
of students. In an empirical study on exam data and in simulations, our
algorithm based on the maximum-likelihood estimator significantly outperforms
simple averaging in prediction accuracy and ex-post fairness even with a small
class and exam size
In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning
Cracks and keyhole pores are detrimental defects in alloys produced by laser
directed energy deposition (LDED). Laser-material interaction sound may hold
information about underlying complex physical events such as crack propagation
and pores formation. However, due to the noisy environment and intricate signal
content, acoustic-based monitoring in LDED has received little attention. This
paper proposes a novel acoustic-based in-situ defect detection strategy in
LDED. The key contribution of this study is to develop an in-situ acoustic
signal denoising, feature extraction, and sound classification pipeline that
incorporates convolutional neural networks (CNN) for online defect prediction.
Microscope images are used to identify locations of the cracks and keyhole
pores within a part. The defect locations are spatiotemporally registered with
acoustic signal. Various acoustic features corresponding to defect-free
regions, cracks, and keyhole pores are extracted and analysed in time-domain,
frequency-domain, and time-frequency representations. The CNN model is trained
to predict defect occurrences using the Mel-Frequency Cepstral Coefficients
(MFCCs) of the lasermaterial interaction sound. The CNN model is compared to
various classic machine learning models trained on the denoised acoustic
dataset and raw acoustic dataset. The validation results shows that the CNN
model trained on the denoised dataset outperforms others with the highest
overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC
score (98%). Furthermore, the trained CNN model can be deployed into an
in-house developed software platform for online quality monitoring. The
proposed strategy is the first study to use acoustic signals with deep learning
for insitu defect detection in LDED process.Comment: 36 Pages, 16 Figures, accepted at journal Additive Manufacturin
Modularizing and Assembling Cognitive Map Learners via Hyperdimensional Computing
Biological organisms must learn how to control their own bodies to achieve
deliberate locomotion, that is, predict their next body position based on their
current position and selected action. Such learning is goal-agnostic with
respect to maximizing (minimizing) an environmental reward (penalty) signal. A
cognitive map learner (CML) is a collection of three separate yet
collaboratively trained artificial neural networks which learn to construct
representations for the node states and edge actions of an arbitrary
bidirectional graph. In so doing, a CML learns how to traverse the graph nodes;
however, the CML does not learn when and why to move from one node state to
another. This work created CMLs with node states expressed as high dimensional
vectors suitable for hyperdimensional computing (HDC), a form of symbolic
machine learning (ML). In so doing, graph knowledge (CML) was segregated from
target node selection (HDC), allowing each ML approach to be trained
independently. The first approach used HDC to engineer an arbitrary number of
hierarchical CMLs, where each graph node state specified target node states for
the next lower level CMLs to traverse to. Second, an HDC-based
stimulus-response experience model was demonstrated per CML. Because
hypervectors may be in superposition with each other, multiple experience
models were added together and run in parallel without any retraining. Lastly,
a CML-HDC ML unit was modularized: trained with proxy symbols such that
arbitrary, application-specific stimulus symbols could be operated upon without
retraining either CML or HDC model. These methods provide a template for
engineering heterogenous ML systems
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Performance analysis of RIS-assisted full-duplex communications with infinite and finite blocklength codes
With the advancement of wireless communication technologies, reconfigurable intelligent surfaces (RISs) have recently paved the way to augmenting the performance of wireless networks with the aid of multiple reflecting surfaces by efficiently attuning the signal reflection through a large number of low-cost passive elements. In this paper, we consider an RIS-aided full-duplex (FD) communication network consisting of a FD access point (AP) that communicates with an uplink and a downlink user simultaneously with the aid of an RIS as well as through the direct link between the AP and users. To evaluate the system performance under infinite blocklength (IBL) and finite blocklength (FBL) codes, we derive the analytical expressions for the outage probability and throughput in case of IBL, and for block-error rate (BLER) and goodput in the case of FBL, for both uplink and downlink transmission. Furthermore, the expressions for the maximum achievable rate under FBL and IBL transmission are derived. Next, we also extend the analysis of the single-user framework to a more practical scenario with multiple users utilizing non-orthogonal multiple access (NOMA) and derive analytical expressions for the outage probability and BLER at each downlink user and at the AP. The accuracy of the derived expressions is validated via simulation results, and insights are provided regarding the impact of the number of reflecting elements and imperfect channel state information (CSI) on the performance of the considered system. Finally, from the comparative analysis, it is shown that the RIS-aided system outperforms the system without RIS in both IBL and FBL scenarios, providing remarkable improvement in the outage probability and BLER
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