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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Datová komunikace v distribučních systémech / Data communication in distribution systems

Sirotný, Miroslav January 2011 (has links)
This project includes a basic overview of concepts as is communication, data communication and distributed to systems. Further focuses on the technology use for remote data collection. Part is dedicated PLC technologies, which use systems for remote data collection. The thesis is also mentioned the concept of quality of electrical energy and standard ČSN EN 50160. The main part is focused on the design, implementation and measurement of the PLC network.
2

Application of Human-computer Interaction Theories to Information Design on Internet Portals

Rao, Sushma 27 November 2002 (has links)
Internet portals are increasingly becoming a primary source of information. A portal is a gateway to information on the Internet or a hub from which users may locate relevant information (Strauss, 2000). Because university Web sites have various user classes, universities are beginning to adopt the portal concept for their Web sites. The study conducted aimed to determine the effect of tailoring information content and presentation style on a university Web portal. User ratings of information design on three metrics and user task performance measures of time and errors were compared for four prototypes. Three prototypes were built on the basis of user requirements and two Human-computer Interaction (HCI) theories and one was a replica of an existing academic information portal. The three metrics were derived from the HCI theories. The contributions of the study are a determination of user acceptance of and user performance with the tailored presentation styles and three metrics derived from HCI theories that can be used to compare alternative information presentation styles for portals. An important contribution is the remote data collection technique that was used in the study and a time-stamping technique that recorded clicks on hyperlinks. / Master of Science
3

Principles for Using Remote Data Collection Devices and Deep Learning in Evaluating Social Impact Indicators of Engineered Products for Global Development

Stringham, Bryan J. 09 December 2022 (has links)
Evaluating the social impacts of engineered products, or effects products have on the daily lives of individuals, is critical to ensuring that products are having positive impacts while avoiding negative impacts and to learning how to improve product designs for a more positive social impact. One approach to quantifying a product's social impact is to use social impact indicators that combine user data in a meaningful way to give insight into the current social condition of an individual or population. However, determining social impact indicators relative to engineered products and individuals in developing countries can be difficult when there is a large geographical distance between the users of a product and those designing them and since many conventional methods of user data collection require direct human interaction with or observation of users of a product. This means user data may only be collected at a single instance in time and infrequently due to the large human resources and cost associated with obtaining them. Alternatively, internet-connected, remote data collection devices paired with deep learning models can provide an effective way to use in-situ sensors to collect data required to calculate social impact indicators remotely, continuously, and less expensively than other methods. This research has identified key principles that can enable researchers, designers, and practitioners to avoid pitfalls and challenges that could be encountered at various stages of the process of using remote sensor devices and deep learning to evaluate social impact indicators of products in developing countries. Chapter 2 introduces a framework that outlines how low-fidelity user data often obtainable using remote sensors or digital technology can be collected and correlated with high-fidelity, infrequently collected user data to enable continuous, remote monitoring of engineered products using deep learning. An example application of this framework demonstrates how it can be used to collect data for calculating several social impact indicators related to water hand pumps in Uganda during a 4 day study. Chapter 3 builds on the framework established in Chapter 2 to provide principles for enabling insights when engaging in long-term deployment of using in-situ sensors and deep learning to monitor the social impact indicators of products in developing countries. These principles were identified while using this approach to monitor the social impact indicators of a water hand pump in Uganda over a 5 month data collection period. Chapter 4 provides principles for successfully developing remote data collection devices used to collect user data for determining social impact indicators. A design tool called the "Social Impact Sensor Canvas" is provided to guide device development along with a discussion of the key decisions, critical questions, common options, and considerations that should be addressed during each stage of device development to increase the likelihood of success. Lastly, Chapter 5 discusses the conclusions made possible through this research along with proposed future work.

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