Algorithm-Based Handling of Complaints Data from the Usage Phase

In this study we investigate how consumers in The Netherlands can be persuaded to adopt sustainable practices when purchasing, using and disposing of clothes. This study investigates the attitude-behavior gap for the sustainable choices for purchase, use and disposing of clothes. For each consumption phase we ran a two-step multiple regression. The findings showed that the importance of the factors vary in the three consumption phases. For purchasing and disposal decisions, the core motivator social motivation predicts sustainable practices best, while it has no role in the usage phase. The factor ability appeared to have a significant role in the disposal phase, but not in the other phases. Finally, the trigger appears to lower the consumers’ ability in the purchasing phase, while it enhances the core motivator social evaluation in the disposal phase.

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Determining the Main Factors Influencing the Energy Consumption of Electric Vehicles in the Usage Phase

Procedia CIRP ◽

10.1016/j.procir.2016.03.014 ◽

2016 ◽

Vol 48 ◽

pp. 352-357 ◽

Cited By ~ 17

Author(s):

Wen Li ◽

Patrick Stanula ◽

Patricia Egede ◽

Sami Kara ◽

Christoph Herrmann

Keyword(s):

Energy Consumption ◽

Electric Vehicles ◽

Usage Phase ◽

Factors Influencing ◽

Main Factors

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Resilience in Mechanical Engineering - A Concept for Controlling Uncertainty during Design, Production and Usage Phase of Load-Carrying Structures

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.885.187 ◽

2018 ◽

Vol 885 ◽

pp. 187-198 ◽

Cited By ~ 5

Author(s):

Lena C. Altherr ◽

Nicolas Brötz ◽

Ingo Dietrich ◽

Tristan Gally ◽

Felix Geßner ◽

...

Keyword(s):

Collaborative Research ◽

Mechanical Engineering ◽

Research Centre ◽

Product Life ◽

Usage Phase ◽

Load Carrying ◽

Design Production ◽

Technical Systems

Resilience as a concept has found its way into different disciplines to describe the ability of an individual or system to withstand and adapt to changes in its environment. In this paper, we provide an overview of the concept in different communities and extend it to the area of mechanical engineering. Furthermore, we present metrics to measure resilience in technical systems and illustrate them by applying them to load-carrying structures. By giving application examples from the Collaborative Research Centre (CRC) 805, we show how the concept of resilience can be used to control uncertainty during different stages of product life.

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Robust optimal usage modeling of product systems for environmental sustainability

Journal of Computational Design and Engineering ◽

10.1016/j.jcde.2018.08.005 ◽

2018 ◽

Vol 6 (3) ◽

pp. 429-435 ◽

Cited By ~ 2

Author(s):

Jungmok Ma

Keyword(s):

Life Cycle ◽

Environmental Sustainability ◽

Product Usage ◽

Numerical Examples ◽

Usage Phase ◽

Multiple Products ◽

Product Systems ◽

Proposed Model ◽

Sustainable Product ◽

The Difference

Abstract Proper modeling of the usage phase in Life Cycle Assessment (LCA) is not only critical due to its high impact among life cycle phases but also challenging due to high variations and uncertainty. Furthermore, when multiple products can be utilized, the optimal product usage should be considered together. The robust optimal usage modeling is proposed in this paper as the framework of usage modeling for LCA with consideration of the uncertainty and optimal usage. The proposed method seeks to optimal product usage in order to minimize the environmental impact of the usage phase under uncertainty. Numerical examples demonstrate the application of the robust optimal usage modeling and the difference from the previous approaches. Highlights The robust optimal usage modeling is proposed for the usage modeling of LCA. The proposed model seeks to sustainable product usage under uncertainty. Numerical examples demonstrate the difference from the previous approaches.

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ICT Usage Phase Survey

PsycTESTS Dataset ◽

10.1037/t64433-000 ◽

2014 ◽

Author(s):

I. Kabakci Yurdakul ◽

A. N. Coklar

Keyword(s):

Usage Phase

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Towards an Approach Integrating Various Levels of Data Analytics to Exploit Product-Usage Information in Product Development

Proceedings of the Design Society: International Conference on Engineering Design ◽

10.1017/dsi.2019.269 ◽

2019 ◽

Vol 1 (1) ◽

pp. 2627-2636 ◽

Cited By ~ 2

Author(s):

Patrick Klein ◽

Wilhelm Frederik van der Vegte ◽

Karl Hribernik ◽

Thoben Klaus-Dieter

Keyword(s):

Product Development ◽

Data Analytics ◽

Complete Picture ◽

Reference Scenario ◽

Comprehensive Understanding ◽

Related Services ◽

Product Usage ◽

Usage Phase ◽

Prescriptive Analytics ◽

Advanced Analytics

AbstractBy applying data analytics to product usage information (PUI) from combinations of different channels, companies can get a more complete picture of their products’ and services’ Mid-Of-Life. All data, which is gathered within the usage phase of a product and which relates to a more comprehensive understanding of the usability of the product itself, can become valuable input. Nevertheless, an efficient use of such knowledge requires to setup related analysis capabilities enabling users not only to visualize relevant data, but providing development related knowledge e.g. to predict product behaviours not yet reflected by initial requirements.The paper elaborates on explorations to support product development with analytics to improve anticipation of future usage of products and related services. The discussed descriptive, predictive and prescriptive analytics in given research context share the idea and overarching process of getting knowledge out of PUI data. By implementation of corresponding features into an open software platform, the application of advanced analytics for white goods product development has been explored as a reference scenario for PUI exploitation.

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APPLYING MODEL-BASED SYSTEMS ENGINEERING TO THE DEVELOPMENT OF AUTONOMOUS VESSEL FUNCTIONS

Proceedings of the Design Society: DESIGN Conference ◽

10.1017/dsd.2020.170 ◽

2020 ◽

Vol 1 ◽

pp. 2455-2464

Author(s):

O. Bleisinger ◽

S. Forte ◽

C. Apostolov ◽

M. Schmitt

Keyword(s):

Complex Systems ◽

Systems Engineering ◽

Usage Phase ◽

Model Based ◽

Civil Sector ◽

External Actors ◽

Model Based Systems Engineering ◽

Autonomous Vessel ◽

Operational Aspects

AbstractDeveloping autonomous functions for complex systems leads to high demands on the consideration of dependencies to external actors in the usage phase. In Model-Based Systems Engineering (MBSE), this can be achieved by modelling operational aspects. Operational aspects are model elements and their relationships to each other. In this contribution, modelling of operational aspects with a MBSE-approach will be demonstrated exemplary on a case study related to the development of a yacht with an autonomous docking assistant. Currently modelling operational aspects is not common in the civil sector.

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Unsupervised Detection of Changes in Usage-Phases of a Mobile App

Applied Sciences ◽

10.3390/app10103656 ◽

2020 ◽

Vol 10 (10) ◽

pp. 3656

Author(s):

Hoyeol Chae ◽

Ryangkyung Kang ◽

Ho-Sik Seok

Keyword(s):

User Interfaces ◽

Mobile Apps ◽

Generative Models ◽

Mobile App ◽

Phase Changes ◽

Machine Learning Techniques ◽

Detection Methods ◽

Scale Invariant ◽

Promising Alternative ◽

Usage Phase

Under the fierce competition and budget constraints, most mobile apps are launched without sufficient tests. Thus, there exists a great demand for automated app testing. Recent developments in various machine learning techniques have made automated app testing a promising alternative to manual testing. This work proposes novel approaches for one of the core functionalities of automated app testing: the detection of changes in usage-phases of a mobile app. Because of the flexibility of app development languages and the lack of standards, each mobile app is very different from other apps. Furthermore, the graphical user interfaces for similar functionalities are rarely consistent or similar. Thus, we propose methods detecting usage-phase changes through object recognition and metrics utilizing graphs and generative models. Contrary to the existing change detection methods requiring learning models, the proposed methods eliminate the burden of training models. This elimination of training is suitable for mobile app testing whose typical usage-phase is composed of less than 10 screenshots. Our experimental results on commercial mobile apps show promising improvement over the state-of-the-practice method based on SIFT (scale-invariant feature transform).

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