873 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
Fully-Automated Packaging Structure Recognition of Standardized Logistics Assets on Images
Innerhalb einer logistischen Lieferkette müssen vielfältige Transportgüter an zahlreichen Knotenpunkten bearbeitet, wiedererkannt und kontrolliert werden. Dabei ist oft ein großer manueller Aufwand erforderlich, um die Paketidentität oder auch die Packstruktur zu erkennen oder zu verifizieren. Solche Schritte sind notwendig, um beispielsweise eine Lieferung auf ihre Vollständigkeit hin zu überprüfen. Wir untersuchen die Konzeption und Implementierung eines Verfahrens zur vollständigen Automatisierung der Erkennung der Packstruktur logistischer Sendungen. Ziel dieses
Verfahrens ist es, basierend auf einem einzigen Farbbild, eine oder mehrere Transporteinheiten akkurat zu lokalisieren und relevante Charakteristika, wie beispielsweise die Gesamtzahl oder die Anordnung der enthaltenen Packstücke, zu erkennen. Wir stellen eine aus mehreren Komponenten bestehende Bildverarbeitungs-Pipeline vor, die diese Aufgabe der Packstrukturerkennung lösen soll.
Unsere erste Implementierung des Verfahrens verwendet mehrere Deep Learning Modelle, genauer gesagt Convolutional Neural Networks zur Instanzsegmentierung, sowie Bildverarbeitungsmethoden und heuristische Komponenten. Wir verwenden einen eigenen Datensatz von Echtbildern aus einer Logistik-Umgebung für Training und Evaluation unseres Verfahrens. Wir zeigen, dass unsere Lösung in der Lage ist, die korrekte Packstruktur in etwa 85% der Testfälle unseres Datensatzes zu erkennen, und sogar eine höhere Genauigkeit erzielt wird, wenn nur die meist vorkommenden Packstücktypen betrachtet werden.
Für eine ausgewählte Bilderkennungs-Komponente unseres Algorithmus vergleichen wir das Potenzial der Verwendung weniger rechenintensiver, eigens designter Bildverarbeitungsmethoden mit den zuvor implementierten Deep Learning Verfahren. Aus dieser Untersuchung schlussfolgern wir die bessere Eignung der lernenden Verfahren, welche wir auf deren sehr gute Fähigkeit zur Generalisierung zurückführen.
Außerdem formulieren wir das Problem der Objekt-Lokalisierung in Bildern anhand selbst gewählter Merkmalspunkte, wie beispielsweise Eckpunkte logistischer Transporteinheiten. Ziel hiervon ist es, Objekte präziser zu lokalisieren, als dies insbesondere im Vergleich zur Verwendung herkömmlicher umgebender Rechtecke möglich ist, während gleichzeitig die Objektform durch bekanntes Vorwissen zur Objektgeometrie forciert wird. Wir stellen ein spezifisches Deep Learning Modell vor, welches die beschriebene Aufgabe löst im Fall von Objekten, welche durch vier Eckpunkte beschrieben
werden können. Das dabei entwickelte Modell mit Namen TetraPackNet wird evaluiert mittels allgemeiner und anwendungsbezogener Metriken. Wir belegen die Anwendbarkeit der Lösung im Falle unserer Bilderkennungs-Pipeline und argumentieren die Relevanz für andere Anwendungsfälle, wie beispielweise Kennzeichenerkennung
An empirical investigation of the relationship between integration, dynamic capabilities and performance in supply chains
This research aimed to develop an empirical understanding of the relationships between integration,
dynamic capabilities and performance in the supply chain domain, based on which, two conceptual
frameworks were constructed to advance the field. The core motivation for the research was that, at
the stage of writing the thesis, the combined relationship between the three concepts had not yet
been examined, although their interrelationships have been studied individually.
To achieve this aim, deductive and inductive reasoning logics were utilised to guide the qualitative
study, which was undertaken via multiple case studies to investigate lines of enquiry that would
address the research questions formulated. This is consistent with the author’s philosophical
adoption of the ontology of relativism and the epistemology of constructionism, which was considered
appropriate to address the research questions. Empirical data and evidence were collected, and
various triangulation techniques were employed to ensure their credibility. Some key features of
grounded theory coding techniques were drawn upon for data coding and analysis, generating two
levels of findings. These revealed that whilst integration and dynamic capabilities were crucial in
improving performance, the performance also informed the former. This reflects a cyclical and
iterative approach rather than one purely based on linearity. Adopting a holistic approach towards
the relationship was key in producing complementary strategies that can deliver sustainable supply
chain performance.
The research makes theoretical, methodological and practical contributions to the field of supply
chain management. The theoretical contribution includes the development of two emerging
conceptual frameworks at the micro and macro levels. The former provides greater specificity, as it
allows meta-analytic evaluation of the three concepts and their dimensions, providing a detailed
insight into their correlations. The latter gives a holistic view of their relationships and how they are
connected, reflecting a middle-range theory that bridges theory and practice. The methodological
contribution lies in presenting models that address gaps associated with the inconsistent use of
terminologies in philosophical assumptions, and lack of rigor in deploying case study research
methods. In terms of its practical contribution, this research offers insights that practitioners could
adopt to enhance their performance. They can do so without necessarily having to forgo certain
desired outcomes using targeted integrative strategies and drawing on their dynamic capabilities
Thermal comfort in indoor sports facilities and the adequacy of demand-controlled ventilation
In sports environments, the unstable occupancy density together with the increased heat and moisture emissions from the sports players imposes an extra duty on the ventilation system. This thesis studies the human thermal response and the adequacy of demand-controlled ventilation in indoor sports facilities as part of the LIIKU project, which investigates the indoor air environment therein and the possible effects on the well-being and health of the occupants.
Several approaches to assess and predict the thermal sensation of sports players were reviewed, including the Fanger's predicted mean vote model. Amongst those, thermo-physiological comfort models were shown to have strong potential to be applied in sports environments where the human activity level is high and constantly changing. Dynamic thermal sensation (DTS) vote from the IESD-FIALA model was proved to be more reliable and superior to the predicted mean vote in sports-related situations.
Ventilation requirements and common practices in indoor sports facilities were investigated, along with the use of demand-controlled ventilation solutions (DCV). The pros and cons of DCV were discussed, particularly the operational challenges. The Latorkartano sports hall in Helsinki was chosen to be studied more closely with an indoor climate and energy simulation in IDA ICE software. In order to predict the thermal sensation of the occupants, the FIALA-IESD model was applied and the DTS was calculated. It was found that reducing the temperature setpoint by 1 to 2oC compared to the current Finnish regulation, which is at 17 to 16oC, could save from 13 to 18% in heating energy of this sports hall without affecting the thermal comfort of the sports players
Optical character recognition on engineering drawings to achieve automation in production quality control
Introduction: Digitization is a crucial step towards achieving automation in production quality control for mechanical products. Engineering drawings are essential carriers of information for production, but their complexity poses a challenge for computer vision. To enable automated quality control, seamless data transfer between analog drawings and CAD/CAM software is necessary.Methods: This paper focuses on autonomous text detection and recognition in engineering drawings. The methodology is divided into five stages. First, image processing techniques are used to classify and identify key elements in the drawing. The output is divided into three elements: information blocks and tables, feature control frames, and the rest of the image. For each element, an OCR pipeline is proposed. The last stage is output generation of the information in table format.Results: The proposed tool, called eDOCr, achieved a precision and recall of 90% in detection, an F1-score of 94% in recognition, and a character error rate of 8%. The tool enables seamless integration between engineering drawings and quality control.Discussion: Most OCR algorithms have limitations when applied to mechanical drawings due to their inherent complexity, including measurements, orientation, tolerances, and special symbols such as geometric dimensioning and tolerancing (GD&T). The eDOCr tool overcomes these limitations and provides a solution for automated quality control.Conclusion: The eDOCr tool provides an effective solution for automated text detection and recognition in engineering drawings. The tool's success demonstrates that automated quality control for mechanical products can be achieved through digitization. The tool is shared with the research community through Github
Intelligent symbol attributes extraction from engineering drawings
Engineering drawings have a lot of information in them that can currently only be extracted by going through them manually. The goal for this thesis was to create a way to automatically extract symbol data from engineering drawings and use the extracted data to make the first demo application. The first application would calculate a complexity value for each drawing based on the extracted data.
The technologies selected for this task included text recognition and computer vision model YOLOv7 that was selected after testing different models. Machine learning based computer vision models were trained with different sized labelled training datasets and tested with a separate set of drawings meant for testing. The total amount of drawings used was 443.
The results achieved in the experiments reached good accuracy. The symbol with the most occurrences was surface roughness with over 5000 occurrences reaching an accuracy of 96\%. But due to problems caused by resolution, text recognition could not be fully implemented. Also, the complexity calculation was not further explored due to more work needed. So, future developments are needed, but the results show promising signs
General Course Catalog [2022/23 academic year]
General Course Catalog, 2022/23 academic yearhttps://repository.stcloudstate.edu/undergencat/1134/thumbnail.jp
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