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Abstract: This in-depth study explores the intricate dynamics of reflective materials, emphasizing their impact on the indoor thermal environment and urban heat island (UHI) mitigation. Examining diffuse highly reflective (DHR), general reflective (GR), and retro-reflective (RR) materials on a simplified building model during summer days, the research utilizes outdoor experiments to analyze air and surface temperatures, as well as solar radiation. Prioritizing key metrics—mean radiant temperature (MRT), operative temperature (OT), and solar reflectance (ρ)—the study uncovers nuanced distinctions in DHR, GR, and RR materials. Solar reflectance calculations consistently show higher values for DHR and RR materials compared to GR material, highlighting reflectance's pivotal role in influencing surface temperatures and indoor thermal environment. When evaluating the impact of exterior wall materials on building temperatures, RR material with a 76% reflectance performs similarly to DHR material (82%). Notably, with a 6% lower reflectance in RR, the temperature contrast between external and internal walls is only about 1.5 °C at its maximum, underscoring RR's effectiveness as an outer wall material for UHI mitigation and building energy conservation, surpassing both DHR and GR materials.
Keywords: urban heat island, reflective materials, indoor thermal environment, solar reflectance, simplified building model
Published in DKUM: 23.08.2024; Views: 110; Downloads: 7
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Abstract: Automatic building reconstruction from laser-scanned data remains a challenging research topic due to buildings’ roof complexity and sparse data. A novel automatic building reconstruction methodology, based on half-spaces and a height jump analysis, is presented in this paper. The proposed methodology is performed in three stages. During the preprocessing stage, the classified input point cloud is clustered by position to obtain building point sets, which are then evaluated to obtain half-spaces and detect height jumps. Half-spaces represent the fundamental shape for generating building models, and their definition is obtained from the corresponding segment of points that describe an individual planar surface. The detection of height jumps is based on a DBSCAN search within a custom search space. During the second stage, the building point sets are divided into sub-buildings in such a way that their roofs do not contain height jumps. The concept of sub-buildings without height jumps is introduced to break down the complex building models with height jumps into smaller parts, where shaping with half-spaces can be applied accurately. Finally, the sub-buildings are reconstructed separately with the corresponding half-spaces and then joined back together to form a complete building model. In the experiments, the methodology’s performance was demonstrated on a large scale and validated on an ISPRS benchmark dataset, where an RMSE of 0.29 m was obtained in terms of the height difference.
Keywords: LiDAR point cloud, building reconstruction, half-spaces, Boolean operations
Published in DKUM: 01.12.2023; Views: 388; Downloads: 23
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Abstract: V magistrskem delu raziščemo področje profiliranja zmogljivosti spletnih strani. Seznanimo se z dejavniki, ki vplivajo na zmogljivost spletnih strani in metrikami, s katerimi se meri njihova zmogljivost. Na osnovi danih metrik razvijemo spletno platformo za samodejno profiliranje zmogljivosti spletnih strani. V rezultatih predstavimo uporabo spletne platforme za profiliranje zmogljivosti čelnih in zalednih delov raznolikih spletnih strani.
Keywords: profiliranje, zmogljivost, spletna stran, uporabniška izkušnja
Published in DKUM: 17.08.2023; Views: 448; Downloads: 92
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Abstract: V magistrskem delu predstavimo nov pristop za napovedovanje multivariatnih časovnih vrst geoprostorskih podatkov. Pripravimo pregled obstoječih pristopov k napovedovanju časovnih vrst prostorskih podatkov. Predstavimo koncepte na katerih temelji konvolucijsko-povratna nevronska mreža ConvLSTM in njeno teoretično osnovo. Z uporabo ConvLSTM pri napovedovanju upoštevamo tako časovne odvisnosti med spremenljivkami, kot tudi prostorske odvnisnosti med podatki v sosednjih točkah. Metodo preizkusimo na primeru napovedovanja več spremenljivk onesnaženosti zraka za več merilnih postaj na različnih lokacijah in jo primerjamo s sorodnimi deli.
Keywords: Multivariatne časovne vrste, geoprostorski podatki, napovedovanje časovnih vrst, konvolucijsko-povratne nevronske mreže
Published in DKUM: 21.10.2022; Views: 543; Downloads: 56
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Abstract: S pomočjo priponskih dreves lahko zelo preprosto in hitro izvajamo različne operacije nad nizi. Za gradnjo priponskih dreves obstajajo različni algoritmi. V diplomskem delu opi-šemo in implementiramo Ukkonenov algoritem, ki priponsko drevo zgradi v linearnem času. Najprej preučimo delovanje algoritma in tvorimo ustrezne podatkovne strukture. Sledi implementacija in preizkušanje. Z eksperimenti pokažemo karakteristike algoritma ob različnem številu znakov ter preverimo njegovo časovno in prostorsko zahtevnost.
Keywords: algoritmi, podatkovne strukture, analiza algoritmov, časovna in prostorska zahtevnost
Published in DKUM: 03.11.2020; Views: 1291; Downloads: 153
Full text (1,21 MB)