Browsing by Author "Nguyen, Ngoc"
Now showing 1 - 3 of 3
- Results Per Page
- Sort Options
- Knowledge transfer in R&D collaboration between university and industry: a multiple-case study of corporate partners of Aalto University
School of Business | Master's thesis(2017) Nguyen, NgocDespite the fact that knowledge transfer has been intensively studied in prior research, the fundamentals that lie at heart of knowledge transfer - people, collaboration forms, and tools have not been paid much attention. The objective of this study was to explore common collaboration forms and commonly used tools to transfer knowledge with an aim to develop a broad overview of knowledge transfer in the context of R&D collaboration between U-I. The research problem was to find out how collaboration forms and tools support knowledge transfer between university and industry. This problem was addressed from two angles. First, the study was set to find out what are common R&D collaboration forms between U-I and how knowledge transfer is supported by each of those forms. Second, the study aimed to discover commonly used tools to transfer knowledge and how they are utilized. The qualitative research was conducted with a multiple-case study approach of five Finnish corporate partners of Aalto University, in which it only shed light on the companies’ perspectives. Thematic data analysis was deployed to analyse data gathered from seven structured interviews, including two main themes: (1) common R&D collaboration forms, and (2) commonly used tools to transfer knowledge. The study showed that R&D collaboration with universities plays an important role in companies’ innovation strategies. Knowledge transfer between university and industry is supported by the collaboration form of contract research, joint research program, and mobility of people. The findings demonstrated that all six steps (identification, recognition, sharing, acquisition, assimilation, and application) of knowledge transfer process occur before, during, and after the collaboration respectively. Face-to-face communication with IT supports and utilization of existing personal contacts is commonly used to transfer knowledge between two organizations whereas web-based platform is not. Besides that, digital platform and database are used to transfer knowledge, yet only for internal use within companies. This research offers a broad overview of knowledge transfer in the context of R&D collaboration between U-I by revealing different common collaboration forms and commonly used tools to support knowledge transfer and condensing them into a theoretical model supported by empirical evidence. As a practical implication of the research, besides utilizing common collaboration forms and tools to support knowledge transfer between university and industry, exploration of new forms and tools is strongly recommended. Although many challenges are to encounter, there are great benefits that new collaboration forms and tools promisingly have to offer. - Predicting the Concentration Values of Individual Chemical Components in Ternary Chemical Mixtures
Perustieteiden korkeakoulu | Master's thesis(2024-06-17) Nguyen, NgocIn the realm of optical sensors for predicting the concentration of ternary solutions, such as a mixture of water, sugar and salt, challenges arise due to the inability to provide specific measurements for each individual component. This thesis proposes a novel approach to address these challenges by employing a machine learning technique. This technique integrates refractive index measurements with other parameters such as conductivity or density, aiming to accurately predict a variety of different ternary chemical compounds. The objectives encompass the identification of a suitable machine learning model, its training and testing on various chemical compounds, and the validation of its performance on Linux Embedded devices with limited computing power. The research methodology includes stages of data collection, model selection, black box engineering, data splitting, model training and evaluation, and performance testing on Raspberry Pi. The anticipated outcome of this research is to enhance the precision of measurements in ternary solutions, thereby contributing to advancements in chemical analysis. The machine learning models introduced in this thesis, employing support vector regression and neural network models, have shown promising initial results. They exhibit an improvement in prediction accuracy when compared to the pre-existing 3𝑟𝑑 degree polynomial models. Furthermore, these developed models are capable of operating on embedded systems with restricted computational resources. - Software-defined Industrial Central Controller for Time-Sensitive Network
Sähkötekniikan korkeakoulu | Bachelor's thesis(2024-04-26) Nguyen, NgocTime Sensitive Network (TSN) is an Ethernet-based technology that is defined by the Institute of Electrical and Electronics Engineers (IEEE) TSN task group. The technology offers capability to deliver reliable network communication. In various TSN functionalities, the network components require controllers for management and time-sensitive related configuration. The thesis goal is to propose an implementation of IEEE-specified centralized controller model for the TSN network. The work involves designing a logical architecture for the controller and creating an efficient test setup with TSN bridges for the controller usage. Furthermore, the thesis investigates the test results to evaluate the controller performance. The thesis key finding is that the IEEE centralized network controller’s model is feasible to implement and provide efficient functionalities on a fixed Ethernet network.