Дисертації з теми "Human-building interactions"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-16 дисертацій для дослідження на тему "Human-building interactions".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте дисертації для різних дисциплін та оформлюйте правильно вашу бібліографію.
Ma, Nuo. "Indoor Human Sensing for Human Building Interaction." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/98916.
Повний текст джерелаMaster of Science
The recent advances in mobile technologies, like smart phones and enhanced wireless communication, allow people to experience added comfort and convenience brought by these devices. For example, smart lighting and air conditioning control can be set remotely, before people arrive at their homes. However, these personal experiences are usually limited to personal spaces and tied to a specific personal smart phone. When it comes to public spaces, we seldom see such technological advancement being utilized. In reality, the concept of smart public spaces is still limited to technologies like opening / closing a door automatically. We discuss the reasons that cause such difference between personal and public spaces. We argue that Human Building Interactions should be shaped around non-intrusive indoor human sensing technologies. We present discussions, considerations and implementation of a system that uses a low cost camera network for indoor human sensing. We also describe several applications based on the developed system. We demonstrate how to bring technology enhanced experiences to public built spaces and provide smart built environments.
Ballivian, Sergio Marlon. "Anonymous Indoor Positioning System using Depth Sensors for Context-aware Human-Building Interaction." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/89612.
Повний текст джерелаMaster of Science
Although Global Positioning System (GPS) has a satisfactory performance navigating outdoors, it fails in indoor environments due to the line of sight requirements. Physical obstacles such as walls, overhead floors, and roofs weaken GPS functionality in closed environments. This limitation has opened a new direction of studies, technologies, and research efforts to create indoor location sensing capabilities. In this study, we have explored the feasibility of using an indoor positioning system that seeks to detect occupants’ location and preferences accurately without raising privacy concerns. Context-aware systems were created to learn dynamics of interactions between human and buildings, examples are sensing, localizing, and distinguishing individuals. An example application is to enable a responsive air-conditioning system to adapt to personalized thermal preferences of occupants in an indoor environment as they move across spaces. To this end, we have proposed to leverage depth sensing technology, such as Microsoft Kinect sensor, that could provide information on human activities and unique skeletal attributes for identification. The proposed sensing technology could enable the inference of people location and preferences at any time and their activity levels across different indoor spaces. This system could be used for sustainable operations in buildings by detecting unoccupied rooms in buildings to save energy and reduce the cost of heating, lighting or air conditioning equipment by delivering air conditioning according to the preferences of occupants. This thesis has explored the feasibility and challenges of using depth-sensing technology for the aforementioned objectives. In doing so, we have conducted experimental studies, as well as data analyses, using different scenarios for human-environment interactions. The results have shown that we could achieve an acceptable level of accuracy in detecting individuals across different spaces for different actions.
Agee, Philip Ryan. "A Macroergonomics Path to Human-centered, Adaptive Buildings." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/102751.
Повний текст джерелаDoctor of Philosophy
Agee, Philip. "A Macroergonomics Path to Human-centered, Adaptive Buildings." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/102751.
Повний текст джерелаDoctor of Philosophy
Afzalan, Milad. "Data-driven customer energy behavior characterization for distributed energy management." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99210.
Повний текст джерелаDoctor of Philosophy
Buildings account for more than 70% of electricity consumption in the U.S., in which more than 40% is associated with the residential sector. During recent years, with the advancement in Information and Communication Technologies (ICT) and the proliferation of data from consumers and devices, data-driven methods have received increasing attention for improving the energy-efficiency initiatives. With the increased adoption of renewable and distributed resources in buildings (e.g., solar panels and storage systems), an important aspect to improve the efficiency by matching the demand and supply is to add flexibility to the energy consumption patterns (e.g., trying to match the times of high energy demand from buildings and renewable generation). In this dissertation, we introduced data-driven solutions using the historical energy data of consumers with application to the flexibility provision. Specific problems include: (1) introducing a ranking score for buildings in a community to detect the candidates that can provide higher energy saving in the future events, (2) estimating the operation time of major energy-intensive appliances by analyzing the whole-house energy data using machine learning models, and (3) investigating the potential of achieving demand-supply balance in communities of buildings under the impact of different levels of solar panels, battery systems, and occupants energy consumption behavior. In the first study, a ranking score was introduced that analyzes the historical energy data from major loads such as washing machines and dishwashers in individual buildings and group the buildings based on their potential for energy saving at different times of the day. The proposed approach was investigated for real data of 400 buildings. The results for EV, washing machine, dishwasher, dryer, and AC show that the approach could successfully rank buildings by their demand reduction potential at critical times of the day. In the second study, machine learning (ML) frameworks were introduced to identify the times of the day that major energy-intensive appliances are operated. To do so, the input of the model was considered as the main circuit electricity information of the whole building either in lower-resolution data (smart meter data) or higher-resolution data (60Hz). Unlike previous studies that required considerable efforts for training the model (e.g, defining specific parameters for mathematical formulation of the appliance model), the aim was to develop data-driven approaches to learn the model either from the same building itself or from the neighbors that have appliance-level metering devices. For the lower-resolution data, the objective was that, if a few samples of buildings have already access to plug meters (i.e., appliance level data), one could estimate the operation time of major appliances through ML models by matching the energy behavior of the buildings, reflected in their smart meter information, with the ones in the neighborhood that have similar behaviors. For the higher-resolution data, an algorithm was introduced that extract the appliance signature (i.e., change in the pattern of electricity signal when an appliance is operated) to create a processed library and match the new events (i.e., times that an appliance is operated) by investigating the similarity with the ones in the processed library. The investigation on major appliances like AC, EV, dryer, and washing machine shows the >80% accuracy on standard performance metrics. In the third study, the impact of adding small-scale distributed resources to individual buildings (solar panels, battery, and users' practice in changing their energy consumption behavior) for matching the demand-supply for the communities was investigated. A community of ~250 buildings was considered to account for realistic uncertain energy behavior across households. It was shown that even when all buildings have a solar panel, during the afternoon times (after 4 pm) in which still ~30% of solar generation is possible, the community could not supply their demand. Furthermore, it was observed that including users' practice in changing their energy consumption behavior and battery could improve the utilization of solar energy around >10%-15%. The results can serve as a guideline for utilities and decision-makers to understand the impact of such different scenarios on improving the utilization of solar adoption. These series of studies in this dissertation contribute to the body of literature by introducing data-driven solutions/investigations for characterizing the energy behavior of households, which could increase the flexibility in energy consumption patterns.
Cao, Hetian. "Designing for Interaction and Insight: Experimental Techniques For Visualizing Building Energy Consumption Data." Research Showcase @ CMU, 2017. http://repository.cmu.edu/theses/130.
Повний текст джерелаKemshal-Bell, Guy Jonathon, and guykb@bigpond net au. "Interactive media - a tool to enhance human communication." RMIT University. Creative Media, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080102.100544.
Повний текст джерелаSateei, Shahin. "VR som verktyg vid kravställning för sjukhusbyggnation." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-422759.
Повний текст джерелаBirgonul, Zeynep. "Symbiotic data platform. A receptive-responsive tool for customizing thermal comfort & optimizing energy efficiency." Doctoral thesis, Universitat Internacional de Catalunya, 2020. http://hdl.handle.net/10803/669180.
Повний текст джерелаKoort, Hannes. "Room for More of Us? : Important Design Features for Informed Decision-Making in BIM-enabled Facility Management." Thesis, Uppsala universitet, Människa-datorinteraktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447217.
Повний текст джерелаAbraham, Johannes, and Robin Romano. "Automatisk kvalitetssäkring av information för järnvägsanläggningar : Automatic quality assurance of information for railway infrastructure." Thesis, KTH, Hälsoinformatik och logistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252832.
Повний текст джерелаWith increased expectations for the expansion of the future railway, this entails an increased load on the current railway network. The result of the expansion can be an increasing number of cancellations and delays. By taking advantage of technological innovations such as digitalization and automation, the existing system and work processes can be developed for more efficient management. The Swedish Transport Administration sets requirements for Building Information Modeling (BIM) in procurements. The planning of signal installations within the railway takes place in Sweco using the CAD program Promis.e. From the program, lists containing the information of the objects (BIS-lists) can be retrieved. The Swedish Transport Administration requires that the attributes must consist of a certain format or have specific values. In this thesis project, methods for automatic quality assurance of infrastructure information and the implementation of the method for rail projects were examined. The investigated methods include the calculation program Excel, the query programming language SQL and the process of ETL. After analyzing the methods, the ETL process was chosen. The result was that a program was created to automatically select the type of BIS list that would be reviewed and to verify that the examined attributes contained allowed values. In order to investigate whether the cost of the programs would benefit the company in addition to the quality assurance, an economic analysis was carried out. According to the calculations, the choice of method could also be justified from an economic perspective.
Huang, Li-Te, and 黃立德. "Development of personalized thermal comfort profiles and AC control strategies through human-building interactions." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/qz477g.
Повний текст джерела國立臺灣大學
土木工程學研究所
107
Nowadays, we spend more than 90% of our time inside of buildings. People rely on more heating, ventilation, and air conditioning (HVAC) systems to maintaining indoor environmental comfort. In order to achieve comfort in the indoor environment, it also costs a huge amount of energy. However, when we maintain the daily operation of the building, we are still don''t know how to fit the thermal comfort needs of the main body of the service: the occupants. Thermal comfort is the condition of mind that expresses satisfaction with the thermal environment. Researchers use many methods want to find indicators that can effectively quantify occupant behavior. One of the indexes have long-term development and also widely known is the predicted mean vote (PMV) index. Each occupant accepts the environmental comfort range is different. Further, when the occupant cannot tolerate thermal comfort and trigger the activity, which is called occupant behavior; and it includes the difference tolerance of each person. Past research has pointed out that occupants directly affect the efficiency of energy use, and therefore develop a method to capture, simulate or establish occupant behavior model is an important topic. In this study, we establish a relationship between PMV index and occupant behavior, and then import the Bayesian approach analysis, to get a personalized occupant thermal comfort profile (model). Then we according to the thermal comfort profile to develop a strategy to control the environment to reduce the interaction between the occupant and the building components (like air conditioner), as the first step of energy saving. The study was conducted experiments through our own cloud management platform, which has environmental data monitoring and air conditioner (AC) control capabilities. The experiments were conducted to verify the effectiveness of the establishment of personalized thermal comfort profiles and AC control strategies. From the experimental results, we can through the occupant interactions with the building components to create a personalized thermal comfort profile, by the online learning system and apply automated control to the environment according to the thermal comfort profile.
He, Yu-En, and 何雨恩. "A Kinect-Based System for Building Human-Computer Interaction Environment and Applications." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/85078800676379249473.
Повний текст джерела國立暨南國際大學
資訊工程學系
102
In the digital age, interactive whiteboards (IWB) gradually replace traditional whiteboards duo to teaching resources are also digitized and published on the Internet. However, hardware-based IWBs are expensive (> 40,000 NT dollars) so that many rural elementary schools are rarely equipped. Therefore, we develop inexpensive software-based IWB for building e-classroom and e-learning environment. We study the development environment of OpenNI and to examine system performance and feasibility of Kinect used to develop the IWB environment. We also implement fingertip identification based on K-Curvature algorithm and integrate with the Microsoft XBox Kinect device to simulate a mouse device. The system also adds the somatosensory mode of Kinect to develop a web-based game platform. Therefore, playing somatosensory games, like Q&A or puzzle games, becomes another add-in function of the system. We design the game engine that integrates with jQuery, HTML5, CSS3, and JS technologies to provide XML descriptions for educators to write Q&A propositions and generate games for classroom learning. This system also provides log mechanism that automatically keeps user log data for adaptive analysis. Finally, we also do experiments on verifying the sensitivity of Kinect and present the Kinect depth histogram as the decision of the optimal distance between Kinect and IWB.
Gonzalez-Abraham, Charlotte E. "Building density and landscape pattern in northern Wisconsin, USA an interaction of human legacies and environment /." 2005. http://catalog.hathitrust.org/api/volumes/oclc/61145472.html.
Повний текст джерелаTypescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 62-65)
(9681032), Xiaoqi Liu. "Exploration of Intelligent HVAC Operation Strategies for Office Buildings." Thesis, 2020.
Знайти повний текст джерелаCommercial buildings not only have significant impacts on occupants’ well-being, but also contribute to more than 19% of the total energy consumption in the United States. Along with improvements in building equipment efficiency and utilization of renewable energy, there has been significant focus on the development of advanced heating, ventilation, and air conditioning (HVAC) system controllers that incorporate predictions (e.g., occupancy patterns, weather forecasts) and current state information to execute optimization-based strategies. For example, model predictive control (MPC) provides a systematic implementation option using a system model and an optimization algorithm to adjust the control setpoints dynamically. This approach automatically satisfies component and operation constraints related to building dynamics, HVAC equipment, etc. However, the wide adaptation of advanced controls still faces several practical challenges: such approaches involve significant engineering effort and require site-specific solutions for complex problems that need to consider uncertain weather forecast and engaging the building occupants. This thesis explores smart building operation strategies to resolve such issues from the following three aspects.
First, the thesis explores a stochastic model predictive control (SMPC) method for the optimal utilization of solar energy in buildings with integrated solar systems. This approach considers the uncertainty in solar irradiance forecast over a prediction horizon, using a new probabilistic time series autoregressive model, calibrated on the sky-cover forecast from a weather service provider. In the optimal control formulation, we model the effect of solar irradiance as non-Gaussian stochastic disturbance affecting the cost and constraints, and the nonconvex cost function is an expectation over the stochastic process. To solve this optimization problem, we introduce a new approximate dynamic programming methodology that represents the optimal cost-to-go functions using Gaussian process, and achieves good solution quality. We use an emulator to evaluate the closed-loop operation of a building-integrated system with a solar-assisted heat pump coupled with radiant floor heating. For the system and climate considered, the SMPC saves up to 44% of the electricity consumption for heating in a winter month, compared to a well-tuned rule-based controller, and it is robust, imposing less uncertainty on thermal comfort violation.
Second, this thesis explores user-interactive thermal environment control systems that aim to increase energy efficiency and occupant satisfaction in office buildings. Towards this goal, we present a new modeling approach of occupant interactions with a temperature control and energy use interface based on utility theory that reveals causal effects in the human decision-making process. The model is a utility function that quantifies occupants’ preference over temperature setpoints incorporating their comfort and energy use considerations. We demonstrate our approach by implementing the user-interactive system in actual office spaces with an energy efficient model predictive HVAC controller. The results show that with the developed interactive system occupants achieved the same level of overall satisfaction with selected setpoints that are closer to temperatures determined by the MPC strategy to reduce energy use. Also, occupants often accept the default MPC setpoints when a significant improvement in the thermal environment conditions is not needed to satisfy their preference. Our results show that the occupants’ overrides can contribute up to 55% of the HVAC energy consumption on average with MPC. The prototype user-interactive system recovered 36% of this additional energy consumption while achieving the same overall occupant satisfaction level. Based on these findings, we propose that the utility model can become a generalized approach to evaluate the design of similar user-interactive systems for different office layouts and building operation scenarios.
Finally, this thesis presents an approach based on meta-reinforcement learning (Meta-RL) that enables autonomous optimal building controls with minimum engineering effort. In reinforcement learning (RL), the controller acts as an agent that executes control actions in response to the real-time building system status and exogenous disturbances according to a policy. The agent has the ability to update the policy towards improving the energy efficiency and occupant satisfaction based on the previously achieved control performance. In order to ensure satisfactory performance upon deployment to a target building, the agent is trained using the Meta-RL algorithm beforehand with a model universe obtained from available building information, which is a probability measure over the possible building dynamical models. Starting from what is learned in the training process, the agent then fine-tunes the policy to adapt to the target building based on-site observations. The control performance and adaptability of the Meta-RL agent is evaluated using an emulator of a private office space over 3 summer months. For the system and climate under consideration, the Meta-RL agent can successfully maintain the indoor air temperature within the first week, and result in only 16% higher energy consumption in the 3rd month than MPC, which serves as the theoretical upper performance bound. It also significantly outperforms the agents trained with conventional RL approach.
Shi, L. "Indoor place classification for intelligent mobile systems." Thesis, 2013. http://hdl.handle.net/10453/28065.
Повний текст джерелаPlace classification is an emerging theme in the study of human-robot interaction which requires common understanding of human-defined concepts between the humans and machines. The requirement posts a significant challenge to the current intelligent mobile systems which are more likely to be operating in absolute coordinate systems, and hence unaware of the semantic labels. Aimed at filling this gap, the objective of the research is to develop an approach for intelligent mobile systems to understand and label the indoor environments in a holistic way based on the sensory observations. Focusing on commonly available sensors and machine learning based solutions which play a significant role in the research of place classification, solutions to train a machine to assign unknown instances with concepts understandable to human beings, like room, office and corridor, in both independent and structured prediction ways, have been proposed in this research. The solution modelling dependencies between random variables, which takes the spatial relationship between observations into consideration, is further extended by integrating the logical coexistence of the objects and the places to provide the machine with the additional object detection ability. The main techniques involve logistic regression, support vector machine, and conditional random field, in both supervised and semi-supervised learning frameworks. Experiments in a variety of environments show convincing place classification results through machine learning based approaches on data collected with either single or multiple sensory modalities; modelling spatial dependencies and introducing semi-supervised learning paradigm further improve the accuracy of the prediction and the generalisation ability of the system; and vision-based object detection can be seamlessly integrated into the learning framework to enhance the discrimination ability and the flexibility of the system. The contributions of this research lie in the in-depth studies on the place classification solutions with independent predictions, the improvements on the generalisation ability of the system through semi-supervised learning paradigm, the formulation of training a conditional random field with partially labelled data, and the integration of multiple cues in two sensory modalities to improve the system's functionality. It is anticipated that the findings of this research will significantly enhance the current capabilities of the human robot interaction and robot-environment interaction.