2,031 research outputs found
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
Image processing and pattern recognition for industrial robotic vision
Imperial Users onl
Cognitive Designers Activity Study, Formalization, Modelling, and Computation
This study aims to explore how designers mentally categorise design information during the early sketching performed in the generative phase. An action research approach is particularly appropriate for identifying the various sorts of design information and the cognitive operations involved in this phase. Thus, we conducted a protocol study with eight product designers based on a descriptive model derived from cognitive psychological memory theories. Subsequent protocol analysis yielded a cognitive model depicting the mental categorisation of design information processing performed by designers. This cognitive model included a structure for design information (high, middle, and low levels) and linked cognitive operations (association and transformation). Finally, this paper concludes by discussing directions for future research on the development of new computational tools for designers
APREGOAR: Development of a geospatial database applied to local news in Lisbon
Project Work presented as the partial requirement for obtaining a Master's degree in Geographic Information Systems and ScienceHá informações valiosas em formato de texto não estruturado sobre a localização, calendarização
e a essências dos eventos disponíveis no conteúdo de notícias digitais. Vários
trabalhos em curso já tentam extrair detalhes de eventos de fontes de notícias digitais,
mas muitas vezes não com a nuance necssária para representar com precisão onde as
coisas realmente acontecem. Alternativamente, os jornalistas poderiam associar manualmente
atributos a eventos descritos nos seus artigos enquanto publicam, melhorando a
exatidão e a confiança nestes atributos espaciais e temporais. Estes atributos poderiam
então estar imediatamente disponíveis para avaliar a cobertura temática, temporal e
espacial do conteúdo de uma agência, bem como melhorar a experiência do utilizador
na exploração do conteúdo, fornecendo dimensões adicionais que podem ser filtradas.
Embora a tecnologia de atribuição de dimensões geoespaciais e temporais para o
emprego de aplicaçãoes voltadas para o consumidor não seja novidade, tem ainda de
ser aplicada à escala das notícias. Além disso, a maioria dos sistemas existentes suporta
apenas uma definição pontual da localização dos artigos, que pode não representar bem
o(s) local(is) real(ais) dos eventos descritos.
Este trabalho define uma aplicação web de código aberto e uma base de dados
espacial subjacente que suporta i) a associação de múltiplos polígonos a representar
o local onde cada evento ocorre, os prazos associados aos eventos, em linha com os
atributos temáticos tradicionais associados aos artigos de notícias; ii) a contextualização
de cada artigo através da adição de mapas de eventos em linha para esclarecer aos
leitores onde os eventos do artigo ocorrem; e iii) a exploração dos corpora adicionados
através de filtros temáticos, espaciais e temporais que exibem os resultados em mapas
de cobertura interactivos e listas de artigos e eventos.
O projeto foi aplicado na área da grande Lisboa de Portugal. Para além da funcionalidade
acima referida, este projeto constroi gazetteers progressivos que podem ser
reutilizados como associações de lugares, ou para uma meta-análise mais aprofundada
do lugar, tal como é percebido coloquialmente. Demonstra a facilidade com que estas
dimensões adicionais podem ser incorporadas com grade confiança na precisão da definição, geridas, e alavancadas para melhorar a gestão de conteúdo das agências noticiosas,
a compreensão dos leitores, a exploração dos investigadores, ou extraídas para
combinação com outros conjuntos dos dados para fornecer conhecimentos adicionais.There is valuable information in unstructured text format about the location, timing,
and nature of events available in digital news content. Several ongoing efforts already
attempt to extract event details from digital news sources, but often not with the
nuance needed to accurately represent the where things actually happen. Alternatively,
journalists could manually associate attributes to events described in their articles while
publishing, improving accuracy and confidence in these spatial and temporal attributes.
These attributes could then be immediately available for evaluating thematic, temporal,
and spatial coverage of an agency’s content, as well as improve the user experience of
content exploration by providing additional dimensions that can be filtered.
Though the technology of assigning geospatial and temporal dimensions for the
employ of consumer-facing applications is not novel, it has yet to be applied at scale to
the news. Additionally, most existing systems only support a single point definition of
article locations, which may not well represent the actual place(s) of events described
within.
This work defines an open source web application and underlying spatial database
that supports i) the association of multiple polygons representing where each event
occurs, time frames associated with the events, inline with the traditional thematic
attributes associated with news articles; ii) the contextualization of each article via the
addition of inline event maps to clarify to readers where the events of the article occur;
and iii) the exploration of the added corpora via thematic, spatial, and temporal filters
that display results in interactive coverage maps and lists of articles and events.
The project was applied to the greater Lisbon area of Portugal. In addition to the
above functionality, this project builds progressive gazetteers that can be reused as place
associations, or for further meta analysis of place as it is colloquially understood. It
demonstrates the ease of which these additional dimensions may be incorporated with a
high confidence in definition accuracy, managed, and leveraged to improve news agency
content management, reader understanding, researcher exploration, or extracted for
combination with other datasets to provide additional insights
Emergent Behaviors in a Resilient Logistics Supply Chain
This PhD dissertation addresses vulnerabilities in logistics supply chains, such as disruptions from pandemics, natural disasters, and geopolitical tensions. It underscores the complexity of supply chains, likening them to socio-technical systems where resilience is key for managing unexpected events and thriving amidst adversity. The focus is on leveraging smart business objects—exemplified by “smart pallets” with sensing and computational capabilities—to augment real-time decision-making and resilience in supply chains. When strategically positioned within the supply network, these smart pallets can provide key insights into the movement of goods, enabling a rapid response to disruptions through real-time monitoring and predictive analytics. The dissertation investigates centralized, decentralized, and hybrid approaches to decision-making within these networks. Centralized methods ensure uniformity but may neglect local specifics, while decentralized ones offer adaptability at the risk of inconsistency. A hybrid model seeks to balance these extremes, combining broad guidelines with local autonomy for optimal resilience. This research aims to explore how such smart objects can anticipate and react to emergent behaviors, thereby augmenting supply chain resilience beyond mere performance indicators to actively managing and adapting to disruptions. Through various chapters, the dissertation offers an exploration, from designing resilient architectures and evaluating business rules in real-time to mining these rules from data and adapting them to evolving circumstances. Overall, this work presents a nuanced view of resilience in supply chains, emphasizing the adaptability of business rules, the importance of technological evolution alongside organizational practices, and the potential of integrating novel techniques such as process mining with multi-agent systems for better decision-making and operational efficiency
Shipping Configuration Optimization with Topology-Based Guided Local Search for Irregular Shaped Shipments
Manufacturer that uses containers to ship products always works to optimize the space inside the containers. Container loading problems (CLP) are widely encountered in forms of raw material flow and handling, product shipments, warehouse management, facility floor planning, as well as strip-packing nesting problems.Investigations and research conducted two decades ago were logistic orientated, on the basis of the empirical approaches
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