Academic literature on the topic '120299 Building not elsewhere classified'
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Journal articles on the topic "120299 Building not elsewhere classified"
Garanča, Biruta. "THE STRUCTURE OF MACHINERY BUILDING IN LATGALE AND PERSPECTIVES OF ITS DEVELOPMENT." Latgale National Economy Research 1, no. 1 (June 30, 2009): 53. http://dx.doi.org/10.17770/lner2009vol1.1.1761.
Full textIndraratna, B., P. Nutalaya, K. S. Koo, and N. Kuganenthira. "Engineering behaviour of a low carbon, pozzolanic fly ash and its potential as a construction fill." Canadian Geotechnical Journal 28, no. 4 (August 1, 1991): 542–55. http://dx.doi.org/10.1139/t91-070.
Full textRosić, Nataša. "Unknown and ill-defined causes of death in the mortality of the populations of Serbia, Croatia, North Macedonia, and Slovenia, in the period between 2007 and 2016." Srpski medicinski casopis Lekarske komore 2, no. 2 (2021): 23–32. http://dx.doi.org/10.5937/smclk2-32461.
Full textShehzad, Sofia. "DENGUE OUTBREAK -IS THE PANIC JUSTIFIED ?" Journal of Gandhara Medical and Dental Science 4, no. 1 (March 20, 2018): 1. http://dx.doi.org/10.37762/jgmds.4-1.224.
Full textShehzad, Sofia. "DENGUE OUTBREAK -IS THE PANIC JUSTIFIED ?" Journal of Gandhara Medical and Dental Science 4, no. 1 (March 20, 2018): 1. http://dx.doi.org/10.37762/jgmds.4-1.224.
Full textAlbert, Stefanie P., Rosa Ergas, Sita Smith, Gillian Haney, and Monina Klevens. "Syndrome Development to Assess IDU, HIV, and Homelessness in MA Emergency Departments." Online Journal of Public Health Informatics 11, no. 1 (May 30, 2019). http://dx.doi.org/10.5210/ojphi.v11i1.9895.
Full textYunxia, Zhu, and Peter Thompson. "Invitation or Sexual Harassment?" M/C Journal 3, no. 4 (August 1, 2000). http://dx.doi.org/10.5204/mcj.1859.
Full textDissertations / Theses on the topic "120299 Building not elsewhere classified"
Parr, Eric. "Performance of an air-to-air heat pump heating and recovery unit at high ventilation rates." Thesis, University of Central Lancashire, 2007. http://clok.uclan.ac.uk/20042/.
Full textOlaniyi, Olayinka Oluseyi. "Development of a facilities management framework for sustainable building practices in Nigeria." Thesis, University of Central Lancashire, 2017. http://clok.uclan.ac.uk/20755/.
Full textOliver, Christine. "Systemic reflexivity : building theory for organisational consultancy." Thesis, University of Bedfordshire, 2012. http://hdl.handle.net/10547/567099.
Full textWong, Kwok Wai Johnny. "Development of selection evaluation and system intelligence analytic models for the intelligent building control systems." Thesis, The Hong Kong Polytechnic University, 2007. https://eprints.qut.edu.au/20343/1/c20343.pdf.
Full textQunby, Rohan G. H. "Time, space, city and resistance : situating Negri's multitude in the contemporary metropolis : a thesis presented in partial fulfilment of the requirements for the degree of Masters in Public Policy at Massey University, Auckland, New Zealand." Massey University, 2009. http://hdl.handle.net/10179/923.
Full text(9750833), Zilong Yang. "Automated Building Extraction from Aerial Imagery with Mask R-CNN." Thesis, 2020.
Find full textBuildings are one of the fundamental sources of geospatial information for urban planning, population estimation, and infrastructure management. Although building extraction research has gained considerable progress through neural network methods, the labeling of training data still requires manual operations which are time-consuming and labor-intensive. Aiming to improve this process, this thesis developed an automated building extraction method based on the boundary following technique and the Mask Regional Convolutional Neural Network (Mask R-CNN) model. First, assisted by known building footprints, a boundary following method was used to automatically best label the training image datasets. In the next step, the Mask R-CNN model was trained with the labeling results and then applied to building extraction. Experiments with datasets of urban areas of Bloomington and Indianapolis with 2016 high resolution aerial images verified the effectiveness of the proposed approach. With the help of existing building footprints, the automatic labeling process took only five seconds for a 500*500 pixel image without human interaction. A 0.951 intersection over union (IoU) between the labeled mask and the ground truth was achieved due to the high quality of the automatic labeling step. In the training process, the Resnet50 network and the feature pyramid network (FPN) were adopted for feature extraction. The region proposal network (RPN) then was trained end-to-end to create region proposals. The performance of the proposed approach was evaluated in terms of building detection and mask segmentation in the two datasets. The building detection results of 40 test tiles respectively in Bloomington and Indianapolis showed that the Mask R-CNN model achieved 0.951 and 0.968 F1-scores. In addition, 84.2% of the newly built buildings in the Indianapolis dataset were successfully detected. According to the segmentation results on these two datasets, the Mask R-CNN model achieved the mean pixel accuracy (MPA) of 92% and 88%, respectively for Bloomington and Indianapolis. It was found that the performance of the mask segmentation and contour extraction became less satisfactory as the building shapes and roofs became more complex. It is expected that the method developed in this thesis can be adapted for large-scale use under varying urban setups.
(10692402), Jorge Alfredo Rojas Rondan. "A BIM-based tool for formwork management in building projects." Thesis, 2021.
Find full text(5930687), Jinglin Jiang. "Investigating How Energy Use Patterns Shape Indoor Nanoaerosol Dynamics in a Net-Zero Energy House." Thesis, 2019.
Find full textResearch on net-zero energy buildings (NZEBs) has been largely centered around improving building energy performance, while little attention has been given to indoor air quality. A critically important class of indoor air pollutants are nanoaerosols – airborne particulate matter smaller than 100 nm in size. Nanoaerosols penetrate deep into the human respiratory system and are associated with deleterious toxicological and human health outcomes. An important step towards improving indoor air quality in NZEBs is understanding how occupants, their activities, and building systems affect the emissions and fate of nanoaerosols. New developments in smart energy monitoring systems and smart thermostats offer a unique opportunity to track occupant activity patterns and the operational status of residential HVAC systems. In this study, we conducted a one-month field campaign in an occupied residential NZEB, the Purdue ReNEWW House, to explore how energy use profiles and smart thermostat data can be used to characterize indoor nanoaerosol dynamics. A Scanning Mobility Particle Sizer and Optical Particle Sizer were used to measure indoor aerosol concentrations and size distributions from 10 to 10,000 nm. AC current sensors were used to monitor electricity consumption of kitchen appliances (cooktop, oven, toaster, microwave, kitchen hood), the air handling unit (AHU), and the energy recovery ventilator (ERV). Two Ecobee smart thermostats informed the fractional amount of supply airflow directed to the basement and main floor. The nanoaerosol concentrations and energy use profiles were integrated with an aerosol physics-based material balance model to quantify nanoaerosol source and loss processes. Cooking activities were found to dominate the emissions of indoor nanoaerosols, often elevating indoor nanoaerosol concentrations beyond 104 cm-3. The emission rates for different cooking appliances varied from 1011 h-1 to 1014 h-1. Loss rates were found to be significantly different between AHU/ERV off and on conditions, with median loss rates of 1.43 h-1 to 3.68 h-1, respectively. Probability density functions of the source and loss rates for different scenarios will be used in Monte Carlo simulations to predict indoor nanoaerosol concentrations in NZEBs using only energy consumption and smart thermostat data.
(10292846), Zhipeng Deng. "RECOGNITION OF BUILDING OCCUPANT BEHAVIORS FROM INDOOR ENVIRONMENT PARAMETERS BY DATA MINING APPROACH." Thesis, 2021.
Find full text(11187477), Jin Wu. "Invariant Signatures for Supporting BIM Interoperability." Thesis, 2021.
Find full textBuilding Information Modeling (BIM) serves as an important media in supporting automation in the architecture, engineering, and construction (AEC) domain. However, with its fast development by different software companies in different applications, data exchange became labor-intensive, costly, and error-prone, which is known as the problem of interoperability. Industry foundation classes (IFC) are widely accepted to be the future of BIM in solving the challenge of BIM interoperability. However, there are practical limitations of the IFC standards, e.g., IFC’s flexibility creates space for misuses of IFC entities. This incorrect semantic information of an object can cause severe problems to downstream uses. To address this problem, the author proposed to use the concept of invariant signatures, which are a new set of features that capture the essence of an AEC object. Based on invariant signatures, the author proposed a rule-based method and a machine learning method for BIM-based AEC object classification, which can be used to detect potential misuses automatically. Detailed categories for beams were tested to have error-free performance. The best performing algorithm developed by the methods achieved 99.6% precision and 99.6% recall in the general building object classification. To promote automation and further improve the interoperability of BIM tasks, the author adopted invariant signature-based object classification in quantity takeoff (QTO), structural analysis, and model validation for automated building code compliance checking (ACC). Automation in such BIM tasks was enabled with high accuracy.
Conference papers on the topic "120299 Building not elsewhere classified"
Stillwell, Ashlynn S., and Michael E. Webber. "Feasibility of Wind Power for Brackish Groundwater Desalination: A Case Study of the Energy-Water Nexus in Texas." In ASME 2010 4th International Conference on Energy Sustainability. ASMEDC, 2010. http://dx.doi.org/10.1115/es2010-90158.
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