scholarly journals An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems

2021 ◽  
Vol 11 (7) ◽  
pp. 3278
Author(s):  
Arpad Gellert ◽  
Stefan-Alexandru Precup ◽  
Bogdan-Constantin Pirvu ◽  
Ugo Fiore ◽  
Constantin-Bala Zamfirescu ◽  
...  
Keyword(s):  
Empirical Evaluation ◽  
Assistance System ◽  
Context Aware ◽  
Factory Workers ◽  
Matching Algorithm ◽  
Partial Matching ◽  
Industrial Iot ◽  
Assistance Systems ◽  
Prediction By Partial Matching ◽  
Assistive Systems

Industrial assistive systems result from a multidisciplinary effort that integrates IoT (and Industrial IoT), Cognetics, and Artificial Intelligence. This paper evaluates the Prediction by Partial Matching algorithm as a component of an assembly assistance system that supports factory workers, by providing choices for the next manufacturing step. The evaluation of the proposed method was performed on datasets collected within an experiment involving trainees and experienced workers. The goal is to find out which method best suits the datasets in order to be integrated afterwards into our context-aware assistance system. The obtained results show that the Prediction by Partial Matching method presents a significant improvement with respect to the existing Markov predictors.

Heart arrhythmia classification using the prediction by partial matching algorithm

2015 ◽  
Vol 52 (4) ◽  
pp. 285 ◽  
Author(s):  
Igor Lucena Peixoto Andrezza ◽  
Erick Vagner Cabral De Lima Borges ◽  
Leonardo Vidal Batista
Keyword(s):  
Matching Algorithm ◽  
Partial Matching ◽  
Heart Arrhythmia ◽  
Prediction By Partial Matching

Классификация научных текстов на основе компрессии аннотаций публикаций

2019 ◽  
pp. 25-38
Author(s):  
И.В. Селиванова ◽  
I.V. Selivanova ◽  
Д.В. Косяков ◽  
D.V. Kosyakov ◽  
А.Е. Гуськов ◽  
...  
Keyword(s):  
Partial Matching ◽  
Prediction By Partial Matching

Исследуется возможность установления смысловой близости научных текстов методом их автоматической классификации, основанным на сжатии аннотаций. Идея метода состоит в том, что алгоритмы компрессии типа PPM (prediction by partial matching) сжимают терминологически близкие тексты существенно лучше, чем далекие. Если для каждой классифицируемой тематики будет сформировано ядро публикаций (аналог обучающей выборки), то наилучшая доля сжатия будет указывать на принадлежность классифицируемого текста к соответствующей тематике. Было определено 30 тематических категорий, каждой из них в базе данных Scopus получены аннотации около 500 публикаций, из которых разными способами выбирались 100 аннотаций для ядра и 20 аннотаций для тестирования. Установлено, что построение ядра на основе высокоцитируемых публикаций выявляет до 12% ошибок против 32% при случайной выборке. На качество классификации влияет и изначальное количество категорий: чем меньше категорий участвует в классификации и чем больше терминологические различия между ними, тем выше её качество.

Digitalisierung von additiven Fertigungseinheiten/Digitalization of additive manufacturing units

2021 ◽  
Vol 111 (09) ◽  
pp. 633-637
Author(s):  
Maximilian Vogt ◽  
Julian Ulrich Weber ◽  
Vishnuu Jothi Prakash
Keyword(s):  
Additive Manufacturing ◽  
Feasible Solution ◽  
Spare Parts ◽  
Assistance System ◽  
Remote Locations ◽  
Assistance Systems ◽  
Mobile Manufacturing ◽  
Manufacturing Technologies

Additive Fertigungstechnologien erlauben die bedarfsgerechte Produktion von individuellen Ersatzteilen. Durch Einsatz mobiler Fertigungseinheiten lässt sich mithilfe dieser Verfahren die Resilienz von isolierten Produktionsstätten erhöhen. Um auch außerfachliches Personal zur Bedienung an entlegenen Einsatzorten zu befähigen, stellen digitale Assistenzsysteme eine mögliche Lösung dar. In diesem Beitrag wird ein solches Assistenzsystem zur Begleitung der manuellen Tätigkeiten beim roboterbasierten DED-Prozess in einer mobilen Fertigungseinheit diskutiert.   Additive manufacturing technologies enable the demand-driven production of individual spare parts. By using mobile manufacturing units, these processes can be used to increase the resilience of isolated production sites. In order to enable non-specialized personnel to operate at remote locations, digital assistance systems are a feasible solution. This paper discusses such an assistance system to accompany manual operations of the robot-based DED process in a mobile manufacturing unit.

Digitale Assistenzsysteme in der Produktion*/Innovative assistance systems in production

2017 ◽  
Vol 107 (03) ◽  
pp. 124-128
Author(s):  
L. Merkel ◽  
J, Starz ◽  
C. Schultz ◽  
S. Braunreuther ◽  
G. Prof. Reinhart
Keyword(s):  
New Technologies ◽  
Assistance System ◽  
Individual Worker ◽  
Best Fitting ◽  
Assistance Systems ◽  
Worker Support ◽  
Selection Of

Digitale Assistenzsysteme in der Produktion helfen, zunehmend komplex werdende Arbeitsaufgaben zu beherrschen. Dafür entstehen im Zuge der Digitalisierung der Produktion forschungsseitig zahlreiche neue Möglichkeiten individueller Werkerunterstützung. Das hier vorgestellte entwickelte Modell gestattet eine detaillierte Beschreibung der Fähigkeiten und Technologien von Komponenten eines Assistenzsystems. Durch einen Abgleich von spezifischen Anforderungen eines Anwendungsfalls mit den Fähigkeiten des Assistenzsystems soll die Auswahl eines geeigneten Assistenzsystems ermöglicht werden.   Digital assistance systems help to master tasks with growing complexity in production. Currently, a lot of research aims at developing new technologies for individual worker support. This paper presents a model for a detailed description of capabilities and technologies used for components in assistance systems. By matching a given task’s requirements with the capabilities of an assistance system, the selection of the best fitting assistance system can be achieved.

scholarly journals Orchestration and Situation Awareness in an Assistance System for Assembly Tasks

2021 ◽  
pp. 332-343
Author(s):  
Wernher Behrendt ◽  
Felix Strohmeier
Keyword(s):  
Situation Awareness ◽  
A Priori ◽  
Assistance System ◽  
Tutoring Systems ◽  
Task Knowledge ◽  
Enterprise Level ◽  
Assistive Systems ◽  
Personal Assistants ◽  
User Friendly ◽  
Assembly Tasks

AbstractWe report on the design, specification and implementation of a situation awareness module used for assistive systems in manufacturing, in the context of Industry 4.0. A recent survey of research done in Germany and Europe, concerning assistive technology in industry shows a very high potential for “intelligent assistance” by combining smart sensors, networking and AI. While the state of the art concerning actual technology in industrial use points more towards user-friendly, speech-based interaction with personal assistants for information retrieval (typically of in-house documentation), the research presented here addresses an enterprise-level assistance system that is supported by a number of specialized Assistance Units that can be customized to the end users’ specifications and that range from tutoring systems to tele-robotics. Key to the approach is situation awareness, which is achieved through a combination of a-priori, task knowledge modelling and dynamic situation assessment on the basis of observation streams coming from sensors, cameras and microphones. The paper describes a working fragment of the industrial task description language and its extensions to cover also the triggering of assistive interventions when the observation modules have sent data that warrants such interventions.

scholarly journals Analyzing Drivers’ Distractions due to Smartphone Usage: Evidence from AutoLog Dataset

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Inayat Khan ◽  
Sanam Shahla Rizvi ◽  
Shah Khusro ◽  
Shaukat Ali ◽  
Tae-Sun Chung
Keyword(s):  
Logistic Regression ◽  
Empirical Study ◽  
User Interfaces ◽  
Empirical Evaluation ◽  
Comprehensive Analysis ◽  
Steering Wheel ◽  
Context Aware ◽  
The Road ◽  
Adaptive User Interfaces ◽  
Driver Distractions

The usage of a smartphone while driving has been declared a global portent and has been admitted as a leading cause of crashes and accidents. Numerous solutions, such as Android Auto and CarPlay, are used to facilitate for the drivers by minimizing driver distractions. However, these solutions restrict smartphone usage, which is impractical in real driving scenarios. This research paper presents a comprehensive analysis of the available solutions to identify issues in smartphone activities. We have used empirical evaluation and dataset-based evaluation to investigate the issues in the existing smartphone user interfaces. The results show that using smartphones while driving can disrupt normal driving and may lead to change the steering wheel abruptly, focus off the road, and increases cognitive load, which could collectively result in a devastating situation. To justify the arguments, we have conducted an empirical study by collecting data using maxed mode survey, i.e., questionnaires and interviews from 98 drivers. The results show that existing smartphone-based solutions are least suitable due to numerous issues (e.g., complex and rich interfaces, redundant and time-consuming activities, requiring much visual and mental attention, and contextual constraints), making their effectiveness less viable for the drivers. Based on findings obtained from Ordinal Logistic Regression (OLR) models, it is recommended that the interactions between the drivers and smartphone could be minimized by developing context-aware adaptive user interfaces to overcome the chances of accidents.

/Digitale Assistenzsysteme in der ProduktionDigital assistance systems in the production area – Specifying the relevant functions

2020 ◽  
Vol 110 (03) ◽  
pp. 103-107
Author(s):  
Christian Bayer ◽  
Rami Makhlouf ◽  
Joachim Metternich
Keyword(s):  
Assistance System ◽  
Production Area ◽  
Assistance Systems

Die Diversifikation von Produkten erhöht die Komplexität in der Produktion, wodurch die Anforderungen an die Beschäftigten steigen. Durch den Einsatz digitaler Assistenzsysteme kann die menschliche Arbeit in der Produktion unterstützt werden. Dieser Beitrag beschäftigt sich mit den relevanten Funktionen eines digitalen Assistenzsystems als Diskussionsgrundlage bei deren Einführung.   The diversification of products makes production more complex and jobs more demanding. Digital assistance systems can support human work in the production area. This article deals with the relevant functions of a digital assistance system as a basis for discussion when implementing them.

Pervasive Computing in the Supermarket

2012 ◽  
pp. 172-185 ◽  
Author(s):  
Darren Black ◽  
Nils Jakob Clemmensen ◽  
Mikael B. Skov
Keyword(s):  
Empirical Study ◽  
Real World ◽  
Context Awareness ◽  
Empirical Evaluation ◽  
Context Aware ◽  
Uniform Pattern ◽  
User Attention ◽  
Context Aware System ◽  
Mobile Context ◽  
Interactive Experience

Shopping in the real world is becoming an increasingly interactive experience as stores integrate various technologies to support shoppers. Based on an empirical study of supermarket shoppers, the authors designed a mobile context-aware system called the Context-Aware Shopping Trolley (CAST). The purpose of CAST is to support shopping in supermarkets through context-awareness and acquiring user attention, thus, the authors’ interactive trolley guides and directs shoppers in the handling and finding of groceries. An empirical evaluation showed that shoppers using CAST behaved differently than shoppers using a traditional trolley. Specifically, shoppers using CAST exhibited a more uniform pattern of product collection and found products more easily while travelling a shorter distance. As such, the study finds that CAST supported the supermarket shopping activity.

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