scholarly journals CLASSIFYING COMPONENT FUNCTION IN PRODUCT ASSEMBLIES WITH GRAPH NEURAL NETWORKS

2021 ◽  
pp. 1-18
Author(s):  
Vincenzo Ferrero ◽  
Bryony DuPont ◽  
Kaveh Hassani ◽  
Daniele Grandi
Keyword(s):  
Product Design ◽  
Expert Knowledge ◽  
Ground Truth ◽  
Tier 2 ◽  
Design Data ◽  
Knowledge Based ◽  
Starting Point ◽  
Graph Neural Networks ◽  
Tier 3 ◽  
Component Function

Abstract Function is defined as the ensemble of tasks that enable the product to complete the designed purpose. Functional tools, such as functional modeling, offer decision guidance in the early phase of product design where explicit design decisions are yet to be made. Function-based design data is often sparse and grounded in individual interpretation. As such, function-based design tools can benefit from automatic function classification to increase data fidelity and provide function representation models that enable function-based intelligent design agents. Function- based design data is commonly stored in manually generated design repositories. These design repositories are a collection of expert knowledge and interpretations of function in product design bounded by function-flow and component taxonomies. In this work, we represent a structured taxonomy-based design repository as assembly-flow graphs, then leverage a graph neural network (GNN) model to perform automatic function classification. We support automated function classification by learn- ing from repository data to establish the ground truth of component function assignment. Experimental results show that our GNN model achieves a micro-average F1-score of 0.832 for tier 1 (broad), 0.756 for tier 2, and 0.783 for tier 3 (specific) functions. Given the imbalance of data features, the results are encouraging. Our efforts in this paper can be a starting point for more sophisticated applications in knowledge-based CAD systems, and Design-for-X consideration in function-based design.

scholarly journals Social Identity Map: A Reflexivity Tool for Practicing Explicit Positionality in Critical Qualitative Research

2019 ◽  
Vol 18 ◽  
pp. 160940691987007 ◽  
Author(s):  
Danielle Jacobson ◽  
Nida Mustafa
Keyword(s):  
Qualitative Research ◽  
Social Identity ◽  
Research Process ◽  
Social Identities ◽  
Tier 2 ◽  
Social Worlds ◽  
Starting Point ◽  
The Social ◽  
Tier 1 ◽  
Tier 3

The way that we as researchers view and interpret our social worlds is impacted by where, when, and how we are socially located and in what society. The position from which we see the world around us impacts our research interests, how we approach the research and participants, the questions we ask, and how we interpret the data. In this article, we argue that it is not a straightforward or easy task to conceptualize and practice positionality. We have developed a Social Identity Map that researchers can use to explicitly identify and reflect on their social identity to address the difficulty that many novice critical qualitative researchers experience when trying to conceptualize their social identities and positionality. The Social Identity Map is not meant to be used as a rigid tool but rather as a flexible starting point to guide researchers to reflect and be reflexive about their social location. The map involves three tiers: the identification of social identities (Tier 1), how these positions impact our life (Tier 2), and details that may be tied to the particularities of our social identity (Tier 3). With the use of this map as a guide, we aim for researchers to be able to better identify and understand their social locations and how they may pose challenges and aspects of ease within the qualitative research process. Being explicit about our social identities allows us (as researchers) to produce reflexive research and give our readers the tools to recognize how we produced the data. Being reflexive about our social identities, particularly in comparison to the social position of our participants, helps us better understand the power relations imbued in our research, further providing an opportunity to be reflexive about how to address this in a responsible and respectful way.

Survey of the State-of-the-Art Expert/Knowledge Based Systems in Civil Engineering.

1986 ◽  
Author(s):  
Simon S. Kim ◽  
Mary Lou Maher ◽  
Raymond E. Levitt ◽  
Martin F. Rooney ◽  
Thomas J. Siller
Keyword(s):  
Civil Engineering ◽  
Expert Knowledge ◽  
State Of The Art ◽  
The State ◽  
Knowledge Based Systems ◽  
Knowledge Based

An Introduction to Nonlinear Chemical Dynamics

1998 ◽  
Author(s):  
Irving R. Epstein ◽  
John A. Pojman
Keyword(s):  
Flow Reactor ◽  
Chemical Dynamics ◽  
Design Data ◽  
Chemical Oscillators ◽  
Starting Point ◽  
Hands On ◽  
Undergraduate Laboratory ◽  
History Of ◽  
Mechanistic Basis ◽  
Nonlinear Chemical Dynamics

Just a few decades ago, chemical oscillations were thought to be exotic reactions of only theoretical interest. Now known to govern an array of physical and biological processes, including the regulation of the heart, these oscillations are being studied by a diverse group across the sciences. This book is the first introduction to nonlinear chemical dynamics written specifically for chemists. It covers oscillating reactions, chaos, and chemical pattern formation, and includes numerous practical suggestions on reactor design, data analysis, and computer simulations. Assuming only an undergraduate knowledge of chemistry, the book is an ideal starting point for research in the field. The book begins with a brief history of nonlinear chemical dynamics and a review of the basic mathematics and chemistry. The authors then provide an extensive overview of nonlinear dynamics, starting with the flow reactor and moving on to a detailed discussion of chemical oscillators. Throughout the authors emphasize the chemical mechanistic basis for self-organization. The overview is followed by a series of chapters on more advanced topics, including complex oscillations, biological systems, polymers, interactions between fields and waves, and Turing patterns. Underscoring the hands-on nature of the material, the book concludes with a series of classroom-tested demonstrations and experiments appropriate for an undergraduate laboratory.

Optimal Timing for Launching Installation of Tier 2 and 3 Systems of School-Wide Positive Behavioral Interventions and Supports

2021 ◽  
pp. 109830072199608
Author(s):  
Angus Kittelman ◽  
Sterett H. Mercer ◽  
Kent McIntosh ◽  
Robert Hoselton
Keyword(s):  
Longitudinal Study ◽  
Behavioral Interventions ◽  
Structural Equation ◽  
Equation Modeling ◽  
Optimal Timing ◽  
First Year ◽  
Tier 2 ◽  
Positive Behavioral Interventions ◽  
Tier 1 ◽  
Tier 3

The purpose of this longitudinal study was to examine patterns in implementation of Tier 2 and 3 school-wide positive behavioral interventions and supports (SWPBIS) systems to identify timings of installation that led to higher implementation of advanced tiers. Extant data from 776 schools in 27 states reporting on the first 3 years of Tier 2 implementation and 359 schools in 23 states reporting on the first year of Tier 3 implementation were analyzed. Using structural equation modeling, we found that higher Tier 1 implementation predicted subsequent Tier 2 and Tier 3 implementation. In addition, waiting 2 or 3 years after initial Tier 1 implementation to launch Tier 2 systems predicted higher initial Tier 2 implementation (compared with implementing the next year). Finally, we found that launching Tier 3 systems after Tier 2 systems, compared with launching both tiers simultaneously, predicted higher Tier 2 implementation in the second and third year, so long as Tier 3 systems were launched within 3 years of Tier 2 systems. These findings provide empirical guidance for when to launch Tier 2 and 3 systems; however, we emphasize that delays in launching advanced systems should not equate to delays in more intensive supports for students.

scholarly journals Global Inland Capture and Culture Finfisheries Follow Different Trends When Evaluated by the Human Development Index

2021 ◽  
Vol 13 (15) ◽  
pp. 8420
Author(s):  
Peter W. Sorensen ◽  
Maria Lourdes D. Palomares
Keyword(s):  
Socioeconomic Factors ◽  
Human Development ◽  
Human Development Index ◽  
Developed Countries ◽  
Global Scale ◽  
Tier 2 ◽  
Development Index ◽  
Tier 1 ◽  
Catch Reconstruction ◽  
Tier 3

To assess whether and how socioeconomic factors might be influencing global freshwater finfisheries, inland fishery data reported to the FAO between 1950 and 2015 were grouped by capture and culture, country human development index, plotted, and compared. We found that while capture inland finfishes have greatly increased on a global scale, this trend is being driven almost entirely by poorly developed (Tier-3) countries which also identify only 17% of their catch. In contrast, capture finfisheries have recently plateaued in moderately-developed (Tier-2) countries which are also identifying 16% of their catch but are dominated by a single country, China. In contrast, reported capture finfisheries are declining in well-developed (Tier-1) countries which identify nearly all (78%) of their fishes. Simultaneously, aquacultural activity has been increasing rapidly in both Tier-2 and Tier-3 countries, but only slowly in Tier-1 countries; remarkably, nearly all cultured species are being identified by all tier groups. These distinctly different trends suggest that socioeconomic factors influence how countries report and conduct capture finfisheries. Reported rapid increases in capture fisheries are worrisome in poorly developed countries because they cannot be explained and thus these fisheries cannot be managed meaningfully even though they depend on them for food. Our descriptive, proof-of-concept study suggests that socioeconomic factors should be considered in future, more sophisticated efforts to understand global freshwater fisheries which might include catch reconstruction.

scholarly journals 23. Regional Variation in Outpatient Antibiotic Prescribing in a Commercially-insured Population, United States, 2017

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S12-S12
Author(s):  
Destani J Bizune ◽  
Danielle Palms ◽  
Laura M King ◽  
Monina Bartoces ◽  
Ruth Link-Gelles ◽  
...  
Keyword(s):  
United States ◽  
Otitis Media ◽  
Acute Otitis Media ◽  
Urgent Care ◽  
Antibiotic Prescribing ◽  
Tier 2 ◽  
Regional Variability ◽  
Patient Age ◽  
Patient Âgé ◽  
Tier 3

Abstract Background Studies have shown that the Southern United States has higher rates of outpatient antibiotic prescribing compared to other regions in the country, but reasons for this variation are unclear. We aimed to determine whether the regional variability in outpatient antibiotic prescribing for respiratory diagnoses can be explained by differences in patient age, care setting, comorbidities, and diagnosis in a commercially-insured population. Methods We analyzed the 2017 IBM® MarketScan® Commercial Database of commercially-insured individuals aged < 65 years. We included visits with acute respiratory tract infection (ARTI) diagnoses from retail clinics, urgent care centers, emergency departments, and physician offices. ARTI diagnoses were categorized as: Tier 1, antibiotics are almost always indicated (pneumonia); Tier 2, antibiotics are sometimes indicated (sinusitis, acute otitis media, pharyngitis); and Tier 3, antibiotics are not indicated (asthma, allergy, bronchitis, bronchiolitis, influenza, nonsuppurative otitis media, viral upper respiratory infections, viral pneumonia). We calculated risk ratios and 95% confidence intervals (CI) stratified by US Census region and ARTI tier using log-binomial models controlling for patient age, comorbidities (Elixhauser and Complex Chronic Conditions for Children), and setting of care, with Tier 3 visits in the West, the strata with the lowest antibiotic prescription rate, as the reference for all strata. Results A total of 100,104,860 visits were analyzed. In multivariable modeling, ARTI visits in the South and Midwest were highly associated with receiving an antibiotic for Tier 2 conditions vs. patients in other regions (Figure 1). Figure 1. Multivariable model comparing risk of receiving an antibiotic for an ARTI by region and diagnostic tier in urgent care, retail health, emergency department, and office visits, MarketScan® 2017, United States Conclusion Regional variability in outpatient antibiotic prescribing for Tier 2 and 3 ARTIs remained even after controlling for patient age, comorbidities, and setting of care. It is likely that this variability is in part due to non-clinical factors such as regional differences in clinicians’ prescribing habits and patient expectations. Targeted and enhanced public health stewardship interventions are needed to address cultural factors that affect antibiotic prescribing in outpatient settings. Disclosures All Authors: No reported disclosures

scholarly journals Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?

Algorithms ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 212
Author(s):  
Youssef Skandarani ◽  
Pierre-Marc Jodoin ◽  
Alain Lalande
Keyword(s):  
Deep Learning ◽  
Cardiac Mri ◽  
Expert Knowledge ◽  
Medical Image Analysis ◽  
Ground Truth ◽  
Cine Mri ◽  
Data Sets ◽  
Mri Segmentation ◽  
Segmentation Evaluation ◽  
Ground Truth Data

Deep learning methods are the de facto solutions to a multitude of medical image analysis tasks. Cardiac MRI segmentation is one such application, which, like many others, requires a large number of annotated data so that a trained network can generalize well. Unfortunately, the process of having a large number of manually curated images by medical experts is both slow and utterly expensive. In this paper, we set out to explore whether expert knowledge is a strict requirement for the creation of annotated data sets on which machine learning can successfully be trained. To do so, we gauged the performance of three segmentation models, namely U-Net, Attention U-Net, and ENet, trained with different loss functions on expert and non-expert ground truth for cardiac cine–MRI segmentation. Evaluation was done with classic segmentation metrics (Dice index and Hausdorff distance) as well as clinical measurements, such as the ventricular ejection fractions and the myocardial mass. The results reveal that generalization performances of a segmentation neural network trained on non-expert ground truth data is, to all practical purposes, as good as that trained on expert ground truth data, particularly when the non-expert receives a decent level of training, highlighting an opportunity for the efficient and cost-effective creation of annotations for cardiac data sets.

scholarly journals Experts versus Algorithms? Optimized Fuzzy Logic Energy Management of Autonomous PV Hybrid Systems with Battery and H2 Storage

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1777
Author(s):  
Lisa Gerlach ◽  
Thilo Bocklisch
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Fuzzy Logic Controller ◽  
Expert Knowledge ◽  
Large Power ◽  
Hybrid Energy ◽  
Knowledge Based ◽  
Long Term Storage ◽  
Fuzzy Logic Approach

Off-grid applications based on intermittent solar power benefit greatly from hybrid energy storage systems consisting of a battery short-term and a hydrogen long-term storage path. An intelligent energy management is required to balance short-, intermediate- and long-term fluctuations in electricity demand and supply, while maximizing system efficiency and minimizing component stress. An energy management was developed that combines the benefits of an expert-knowledge based fuzzy logic approach with a metaheuristic particle swarm optimization. Unlike in most existing work, interpretability of the optimized fuzzy logic controller is maintained, allowing the expert to evaluate and adjust it if deemed necessary. The energy management was tested with 65 1-year household load datasets. It was shown that the expert tuned controller is more robust to changes in load pattern then the optimized controller. However, simple readjustments restore robustness, while largely retaining the benefits achieved through optimization. Nevertheless, it was demonstrated that there is no one-size-fits-all tuning. Especially, large power peaks on the demand-side require overly conservative tunings. This is not desirable in situations where such peaks can be avoided through other means.

scholarly journals How does the robot feel? Perception of valence and arousal in emotional body language

2018 ◽  
Vol 9 (1) ◽  
pp. 168-182 ◽  
Author(s):  
Mina Marmpena ◽  
Angelica Lim ◽  
Torbjørn S. Dahl
Keyword(s):  
Human Perception ◽  
Ground Truth ◽  
Body Language ◽  
Human Robot Interaction ◽  
Behavioral Experiments ◽  
Robot Interaction ◽  
Expression Of Emotion ◽  
Exploratory Approach ◽  
Starting Point ◽  
Valence And Arousal

Abstract Human-robot interaction in social robotics applications could be greatly enhanced by robotic behaviors that incorporate emotional body language. Using as our starting point a set of pre-designed, emotion conveying animations that have been created by professional animators for the Pepper robot, we seek to explore how humans perceive their affect content, and to increase their usability by annotating them with reliable labels of valence and arousal, in a continuous interval space. We conducted an experiment with 20 participants who were presented with the animations and rated them in the two-dimensional affect space. An inter-rater reliability analysis was applied to support the aggregation of the ratings for deriving the final labels. The set of emotional body language animations with the labels of valence and arousal is available and can potentially be useful to other researchers as a ground truth for behavioral experiments on robotic expression of emotion, or for the automatic selection of robotic emotional behaviors with respect to valence and arousal. To further utilize the data we collected, we analyzed it with an exploratory approach and we present some interesting trends with regard to the human perception of Pepper’s emotional body language, that might be worth further investigation.

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