| 1. |
- Aeddula, Omsri, 1993-, et al.
(author)
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A Solution with Bluetooth Low Energy Technology to Support Oral Healthcare Decisions for improving Oral Hygiene
- 2021
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In: ACM International Conference Proceeding Series. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450389846 ; , s. 134-139
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Conference paper (peer-reviewed)abstract
- The advent of powered toothbrushes and associated mobile health applications provides an opportunity to collect and monitor the data, however collecting reliable and standardized data from large populations has been associated with efforts from the participants and researchers. Finding a way to collect data autonomously and without the need for cooperation imparts the potential to build large knowledge banks. A solution with Bluetooth low energy technology is designed to pair a powered toothbrush with a single-core processor to collect raw data in a real-time scenario, eliminating the manual transfer of powered toothbrush data with mobile health applications. Associating powered toothbrush with a single-core processor is believed to provide reliable and comprehensible data of toothbrush use and propensities can be a guide to improve individual exhortation and general plans on oral hygiene quantifies that can prompt improved oral wellbeing. The method makes a case for an expanded chance to plan assistant capacities to protect or improve factors that influence oral wellbeing in individuals with mild cognitive impairment. The proposed framework assists with determining various parameters, which makes it adaptable and conceivable to execute in various oral care contexts
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| 2. |
- Aeddula, Omsri, 1993-, et al.
(author)
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AI-Driven Comprehension of Autonomous Construction Equipment Behavior for Improved PSS Development
- 2024
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In: Proceedings of the 57th Annual Hawaii International Conference on System Sciences. - : IEEE Computer Society. - 9780998133171 ; , s. 1017-1026
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Conference paper (peer-reviewed)abstract
- This paper presents an approach that utilizes artificial intelligence techniques to identify autonomous machine behavior patterns. The context for investigation involves a fleet of prototype autonomous haulers as part of a Product Service System solution under development in the construction and mining industry. The approach involves using deep learning-based object detection and computer vision to understand how prototype machines operate in different situations. The trained model accurately predicts and tracks the loaded and unloaded machines and helps to identify the data patterns such as course deviations, machine failures, unexpected slowdowns, battery life, machine activity, number of cycles per charge, and speed. PSS solutions hinge on efficiently allocating resources to meet the required site-level output. Solution providers can make more informed decisions at the earlier stages of development by using the AI techniques outlined in the paper, considering asset management and reallocation of resources to account for unplanned stoppages or unexpected slowdowns. Understanding machine behavioral aspects in early-stage PSS development could enable more efficient and customized PSS solutions.
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| 3. |
- Aeddula, Omsri, 1993-, et al.
(author)
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AI-driven Ossification Assessment in Knee MRI : A Product-Service System Development for Informed Clinical Decision-Making
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Other publication (other academic/artistic)abstract
- Background: Traditionally, assessing the degree of ossification in the epiphyseal plate for growth plate development relies on manual evaluation, which can be inefficient due to the complexities of the distal femoral epiphysis anatomy. Existing methods lack efficient detection techniques.Method: This study proposes an AI-based decision support system, designed within a product-service system (PSS) framework, to automate ossification assessment and detection of the distal femoral epiphysis in knee magnetic resonance imaging (MRI) data. The system leverages advanced machine learning techniques, specifically two Convolutional Neural Networks (CNNs), combined with computer vision techniques. This intelligent system analyzes MRI slices to predict the optimal slice for analysis and identify variations in the degree of ossification within individual datasets.Results: The proposed method's effectiveness is demonstrated using a set of T2-weighted gradient echo grayscale knee MRI data. The system successfully detects the complex anatomy of the distal femoral epiphysis, revealing variations in the degree of ossification ranging from completely closed/open to fully open/closed regions.Conclusions: This study presents a robust and efficient AI-based method, integrated within a PSS framework, for measuring the degree of ossification in the distal femoral epiphysis. This approach automates ossification assessment, providing valuable insights for clinical decision-making by clinicians and forensic practitioners. The PSS framework ensures seamless integration of the AI technology into existing workflows.
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| 4. |
- Aeddula, Omsri, 1993-, et al.
(author)
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AI-Driven Predictive Maintenance for Autonomous Vehicles for Product-Service System Development
- 2024
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Conference paper (peer-reviewed)abstract
- The paper presents an Artificial Intelligence-driven approach to predictive maintenance for Product-Service System (PSS) development. This study focuses on time-based and condition-based maintenance, leveraging variational autoencoders to identify both predicted and unpredicted maintenance issues in autonomous haulers. By analyzing data patterns and forecasting future values, this approach enables proactive maintenance and informed decision-making in the early stages of PSS development. The inclusion of interaction terms enhances the model’s ability to capture the interdependencies among system components, addressing hidden failure modes. Comprehensive evaluations demonstrate the effectiveness and robustness of the developed models, showcasing resilience to noise and variations in operational data. The integration of predictive maintenance with PSS development offers a strategic advantage, providing insights into vehicle performance early in the development phases. This empowers decision-makers for efficient resource allocation and proactive maintenance planning. The research highlights the limitations and potential areas of improvement while also emphasizing the practical applicability and significance of the developed models in enhancing PSS development.
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| 5. |
- Aeddula, Omsri, 1993-, et al.
(author)
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Artificial Neural Networks Supporting Cause-and-Effect Studies in Product–Service System Development
- 2021
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In: Design for Tomorrow—Volume 2. - Singapore : Springer. - 9789811601187 ; , s. 53-64
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Conference paper (peer-reviewed)abstract
- A data analysis method based on artificial neural networks aiming to support cause-and-effect analysis in design exploration studies is presented. The method clusters and aggregates the effects of multiple design variables based on the structural hierarchy of the evaluated system. The proposed method is exemplified in a case study showing that the predictive capability of the created, clustered, a dataset is comparable to the original, unmodified, one. The proposed method is evaluated using coefficient-of-determination, root mean square error, average relative error, and mean square error. Data analysis approach with artificial neural networks is believed to significantly improve the comprehensibility of the evaluated cause-and-effect relationships studying PSS concepts in a cross-functional team and thereby assisting the difficult and resource-demanding negotiations process at the conceptual stage of the design.
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| 6. |
- Aeddula, Omsri, 1993-
(author)
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Data-Driven Decision Support Systems for Product Development - A Data Exploration Study Using Machine Learning
- 2021
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Licentiate thesis (other academic/artistic)abstract
- Modern product development is a complex chain of events and decisions. The ongoing digital transformation of society, increasing demands in innovative solutions puts pressure on organizations to maintain, or increase competitiveness. As a consequence, a major challenge in the product development is the search for information, analysis, and the build of knowledge. This is even more challenging when the design element comprises complex structural hierarchy and limited data generation capabilities. This challenge is even more pronounced in the conceptual stage of product development where information is scarce, vague, and potentially conflicting. The ability to conduct exploration of high-level useful information using a machine learning approach in the conceptual design stage would hence enhance be of importance to support the design decision-makers, where the decisions made at this stage impact the success of overall product development process.The thesis aims to investigate the conceptual stage of product development, proposing methods and tools in order to support the decision-making process by the building of data-driven decision support systems. The study highlights how the data can be utilized and visualized to extract useful information in design exploration studies at the conceptual stage of product development. The ability to build data-driven decision support systems in the early phases facilitates more informed decisions.The thesis presents initial descriptive study findings from the empirical studies, showing the capabilities of the machine learning approaches in extracting useful information, and building data-driven decision support systems. The thesis initially describes how the linear regression model and artificial neural networks extract useful information in design exploration, providing support for the decision-makers to understand the consequences of the design choices through cause-and-effect relationships on a detailed level. Furthermore, the presented approach also provides input to a novel visualization construct intended to enhance comprehensibility within cross-functional design teams. The thesis further studies how the data can be augmented and analyzed to extract the necessary information from an existing design element to support the decision-making process in an oral healthcare context.
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| 7. |
- Aeddula, Omsri, 1993-, et al.
(author)
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Image-Based Localization System
- 2020
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In: Proceedings of the 8th ICIECE 2019. - Singapore : Springer. ; , s. 535-541
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Conference paper (peer-reviewed)abstract
- The position of a vehicle is essential for navigation of the vehicle along the desired path without a human interference. A good positioning system should have both good positioning accuracy and reliability. Global Positioning System (GPS) employed for navigation in a vehicle may lose significant power due to signal attenuation caused by construction buildings or other obstacles. In this paper, a novel real-time indoor positioning system using a static camera is presented. The proposed positioning system exploits gradient information evaluated on the camera video stream to recognize the contours of the vehicle. Subsequently, the mass center of the vehicle contour is used for simultaneous localization of the vehicle. This solution minimizes the design and computational complexity of the positioning system. The experimental evaluation of the proposed approach has demonstrated the positioned accuracy of 92.26%. © Springer Nature Singapore Pte Ltd. 2020.
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| 8. |
- Aeddula, Omsri, 1993-
(author)
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Navigating Data Challenges: AI-Driven Decision Support for Product-Service System Development
- 2024
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Doctoral thesis (other academic/artistic)abstract
- Solution providers are transitioning from product-centric models to service-oriented solutions. This shift has led to the rise of Product-Service Systems (PSS), which offer a holistic approach by integrating physical products with associated services. However, the inherent complexity and collaborative nature of PSS development present a significant challenge: information gathering, analysis, and knowledge building. This is further amplified in the early stages of PSS development due to data challenges such as uncertainty, ambiguity, and complexity. This complicates informed decision-making, potentially leading to the risk of sub-optimal outcomes and impacting the success of final offerings.This research proposes an AI-powered data analysis approach to address these data challenges and augment the decision-making process of PSS development. The focus is on supporting early-stage decision-making, as decisions made at this stage greatly impact the success of final solutions. The research investigates how data can be utilized and visualized to extract actionable insights, ultimately facilitating informed decision-making.The presented research demonstrates that AI-powered data analysis effectively supports informed decision-making in early-stage PSS development. By extracting actionable insights from complex data, handling data limitations, and enabling informed strategic decisions, knowledge sharing, and collaboration are facilitated among stakeholders. Furthermore, integrating AI with visualization tools fosters knowledge building and a deeper understanding of system behavior, ultimately leading to more successful PSS solutions. The efficacy of AI-powered data analysis for handling diverse data types across application domains is demonstrated, potentially leading to benefits such as a deeper understanding of system behavior and proactive solution strategies. These advancements contribute to developing decision support systems specifically for PSS development.Overall, this research demonstrates the efficacy of AI-powered data analysis in overcoming data challenges and empowering decision-makers in early-stage PSS development. This translates to more informed choices, leading to the creation of successful and efficient PSS solutions.
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| 9. |
- Barlo, Alexander, M.Sc. Eng. 1994-, et al.
(author)
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Creating a Virtual Shadow of the Manufacturing of Automotive Components
- 2024
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Conference paper (peer-reviewed)abstract
- Within the automotive industry, there is an increasing demand for a paradigmshift in terms of which materials are used for the manufacturing of the automotive body. Globalclimate goals are forcing a rapid adaption of new, advanced, sustainable material grades suchas the fossil free steels and materials containing higher scrap content. With the introduction ofthese new and untested materials, methods for accounting for variation in material propertiesare needed directly in the press lines.The following study will focus on creating an initial virtual shadow of the manufacturing of aVolvo XC90 inner door panel through the application of Artificial Neural Networks (ANN). Thevirtual shadow differs from the concept of the digital twin by only being a virtual representationof the production line, with training data generated exclusively by numerical simulations, andhaving no automated communication with the physical press line control system. The virtualshadow can be used as an assistance to the press line operators to see how different press linesettings and material parameter variations will impact the quality of the stamped component.The study aims to validate the virtual shadow through accurate predictions of the materialdraw-in measured in the physical press line.
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| 10. |
- Machchhar, Raj Jiten, 1991-, et al.
(author)
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SUPPORTING CHANGEABILITY QUANTIFICATION IN PRODUCT-SERVICE SYSTEMS VIA CLUSTERING ALGORITHM
- 2023
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In: Proceedings of the Design Society. - : Cambridge University Press. ; , s. 3225-3234
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Conference paper (peer-reviewed)abstract
- The design of Product-Service Systems (PSS) is challenging due to the inherent complexities and the associated uncertainties. This challenge aggravates when the PSS being considered has a longer lifespan, is expected to encounter a dynamic context, and integrates many novel technologies. From systems engineering literature, one of the measures for mitigating the risks associated with the uncertainties is incorporating means in the system to change internally as a response to change externally. Such systems are referred to as value-robust systems, and their development largely relies on Tradespace exploration and synthesis. Tradespace exploration and synthesis can be challenging and a time-consuming task due to dimensionality. In this light, this paper aims to present an approach that enables the population of the Tradespace and then, supports the synthesis of such a Tradespace using a clustering algorithm for support changeability quantification in PSS. The proposed method is also implemented on a demonstrative case from the construction machinery industry.
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| 11. |
- Nygaard Ege, Daniel, et al.
(author)
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VIRTUALLY HOSTED HACKATHONS FOR DESIGN RESEARCH : LESSONS LEARNED FROM THE INTERNATIONAL DESIGN ENGINEERING ANNUAL (IDEA) CHALLENGE 2022
- 2023
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In: Proceedings of the Design Society. - : Cambridge University Press. ; , s. 3811-3820
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Conference paper (peer-reviewed)abstract
- The International Design Engineering Annual (IDEA) Challenge is a virtually hosted hackathon for Engineering Design researchers with aims of: i) generating open access datasets; ii) fostering community between researchers; and, iii) applying great design minds to develop solutions to real design problems. This paper presents the 2022 IDEA challenge and elements of the captured dataset with the aim of providing insights into prototyping behaviours at virtually hosted hackathons, comparing it with the 2021 challenge dataset and providing reflections and learnings from two years of running the challenge. The dataset is shown to provide valuable insights into how designers spend their time at hackathon events and how, why and when prototypes are used during their design processes. The dataset also corroborates the findings from the 2021 dataset, demonstrating the complementarity of physical and sketch prototypes. With this paper, we also invite the wider community to contribute to the IDEA Challenge in future years, either as participants or in using the platform to run their own design studies. © The Author(s), 2023. Published by Cambridge University Press.
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| 12. |
- Wall, Johan, et al.
(author)
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Data analysis method supporting cause and effect studies in product-service system development
- 2020
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In: DESIGN 2020 - 16th International Design Conference. - : Cambridge University Press. ; , s. 461-470
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Conference paper (peer-reviewed)abstract
- A data analysis method aiming to support cause and effect analysis in design exploration studies is presented. The method clusters and aggregates effects of multiple design variables based on the structural hierarchy of the evaluated system. The resulting dataset is intended as input to a visualization construct based on colour-coding CAD models. The proposed method is exemplified in a case study showing that the predictive capability of the created, clustered, dataset is comparable to the original, unmodified, one
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