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  • Presentation: 2024-02-29 09:30 Sal J1630, Karlskrona
    Zhang, Yan
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för maskinteknik.
    Abstract [en]

    There is a gradually spreading servitization trend that is forcing industrial manufacturing companies acting in the global market to rethink their business. Manufacturing companies that were traditionally perceived as product-centered, are today increasingly influenced by a service-oriented theory, which claims that manufacturing companies are driven to shift their business focus towards a strategy where customer-perceived value is in the spotlight, and where products are bundled with services to offer Product-Service Systems (PSS). The need to integrate several knowledge domains (i.e. product development, service development, recycling, etc.) means that industry companies need to move “downstream” knowledge from the entire lifecycle into the early phases of the PSS design process where critical decisions are made. At the same time, this raises the awareness of, and requirements for, methods and tools that support cross-disciplinary team collaboration in the process of designing these PSS solutions. Value co-creation is one strategy to address customer collaboration to develop PSS in a framework that allows different stakeholders to participate in defining design concepts and finding the optimal combination of hardware and service that supplies the desired value. Value co-creation strategy and global collaborative innovation are essential for manufacturing companies to explore new ways of designing PSS.

    The thesis summarizes the research performed by the author, as an industrial Ph.D. student and director for system innovation at BIGmind Innovation. This thesis aims to study and explore the motivation for, and challenges of, working with value co-creation for PSS design by global manufacturing companies. Firstly, the empirical research determined that there are different challenges that global manufacturing companies and product development face when designing PSS. The work involves exploring value co-creation via a customer collaborative design platform and experience prototyping for product-service system design. The research shows that there is a lack of knowledge about guidelines and processes for collaboration in value co-creation. The research emphasizes that the PSS design methodologies of today neglect to specify the roles and responsibilities of the actors who co-create PSS offerings, and there is a lack of understanding of the entire process and how it is implemented in industrial practice when developing solutions.

    Conclusions from this work suggest that government policies can make a fast and major impact on the demand for innovations and PSS development. Additionally, a value co-creation approach promotes large-scale user participation in the early phase of PSS design. To enhance stakeholder participation and gather feedback, experiential prototypes were utilized during the conceptual design phase of the PSS design. To support further development in the area of value co-creation, the Future Innovation Framework (FIF) is proposed as a mechanism to facilitate the adoption and use of value co-creation of PSS design. This thesis discusses the implications, opportunities, and challenges of the FIF for industrial PSS design. The thesis concludes with a discussion on the possibility of using value co-creation for PSS design in different industry domains in the future. 

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  • Presentation: 2024-05-10 13:05 Karlskrona
    Idrisoglu, Alper
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för hälsa. Blekinge Institute of Technology.
    Voice for Decision Support in Healthcare Applied to Chronic Obstructive Pulmonary Disease Classification: A Machine Learning Approach2024Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Background: Advancements in machine learning (ML) techniques and voice technology offer the potential to harness voice as a new tool for developing decision-support tools in healthcare for the benefit of both healthcare providers and patients. Motivated by technological breakthroughs and the increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare, numerous studies aim to investigate the diagnostic potential of ML algorithms in the context of voice-affecting disorders. This thesis focuses on respiratory diseases such as Chronic Obstructive Pulmonary Disease (COPD) and explores the potential of a decision support tool that utilizes voice and ML. This exploration exemplifies the intricate relationship between voice and overall health through the lens of applied health technology (AHT. This interdisciplinary nature of research recognizes the need for accurate and efficient diagnostic tools.

    Objective: The objectives of this licentiate thesis are twofold. Firstly, a Systematic Literature Review (SLR) thoroughly investigates the current state of ML algorithms in detecting voice-affecting disorders, pinpointing existing gaps and suggesting directions for future research. Secondly, the study focuses on respiratory health, specifically COPD, employing ML techniques with a distinct emphasis on the vowel "A". The aim is to explore hidden information that could potentially be utilized for the binary classification of COPD vs no COPD. The creation of a new Swedish COPD voice classification dataset is anticipated to enhance the experimental and exploratory dimensions of the research.

    Methods: In order to have a holistic view of a research field, one of the commonly utilized methods is to scan and analyze the literature. Therefore, Paper I followed the methodology of an SLR where existing journal publications were scanned and synthesized to create a holistic view in the realm of ML techniques employed to experiment on voice-affecting disorders. Based on the results from the SLR, Paper II focused on the data collection and experimentation for the binary classification of COPD, which was one of the gaps identified in the first study. Three distinct ML algorithms were investigated on the collected datasets through voice features, which consisted of recordings collected through a mobile application from participants 18 years old and above, and the most utilized performance measures were computed for the best outcome. 

    Results: The summary of findings from Paper I reveals the dominance of Support Vector Machine (SVM) classifiers in voice disorder research, with Parkinson's Disease and Alzheimer's Disease as the most studied disorders. Gaps in research include underrepresented disorders, limited datasets in terms of number of participants, and a lack of interest in longitudinal studies. Paper II demonstrates promising results in COPD classification using ML and a newly developed dataset, offering insights into potential decision support tools for COPD diagnosis.

    Conclusion: The studies covered in this dissertation provide a comprehensive literature summary of ML techniques used to support decision-making on voice-affecting disorders for clinical outcomes. The findings contribute to understanding the diagnostic potential of using ML on vocal features and highlight avenues for future research and technology development. Nonetheless, the experiment reveals the potential of employing voice as a digital biomarker for COPD diagnosis using ML.