Academic literature on the topic 'Multi-accuracy spatial data'
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Journal articles on the topic "Multi-accuracy spatial data"
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Belussi, Alberto, and Sara Migliorini. "A framework for integrating multi-accuracy spatial data in geographical applications." GeoInformatica 16, no. 3 (October 20, 2011): 523–61. http://dx.doi.org/10.1007/s10707-011-0140-9.
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Jeong, Weonil. "Multi-level Load Shedding Scheme to Increase Spatial Data Stream Query Accuracy." Journal of the Korea Academia-Industrial cooperation Society 16, no. 12 (December 31, 2015): 8370–77. http://dx.doi.org/10.5762/kais.2015.16.12.8370.
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Järv, Olle, Henrikki Tenkanen, and Tuuli Toivonen. "Enhancing spatial accuracy of mobile phone data using multi-temporal dasymetric interpolation." International Journal of Geographical Information Science 31, no. 8 (February 7, 2017): 1630–51. http://dx.doi.org/10.1080/13658816.2017.1287369.
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Shimizu, Katsuto, Tetsuji Ota, Nobuya Mizoue, and Hideki Saito. "Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests." Remote Sensing 12, no. 11 (June 9, 2020): 1876. http://dx.doi.org/10.3390/rs12111876.
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Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.
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Tu, Jinsheng, Haohan Wei, Rui Zhang, Lei Yang, Jichao Lv, Xiaoming Li, Shihai Nie, Peng Li, Yanxia Wang, and Nan Li. "GNSS-IR Snow Depth Retrieval from Multi-GNSS and Multi-Frequency Data." Remote Sensing 13, no. 21 (October 26, 2021): 4311. http://dx.doi.org/10.3390/rs13214311.
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Global navigation satellite system interferometric reflectometry (GNSS-IR) represents an extra method to detect snow depth for climate research and water cycle managing. However, using a single frequency of GNSS-IR for snow depth retrieval is often found to be challenging when attempting to achieve a high spatial and temporal sensitivity. To evaluate both the capability of the GNSS-IR snow depth retrieved by the multi-GNSS system and multi-frequency from signal-to-noise ratio (SNR) data, the accuracy of snow depth retrieval by different frequency signals from the multi-GNSS system is analyzed, and a joint retrieval is carried out by combining the multi-GNSS system retrieval results. The SNR data of the global positioning system (GPS), global orbit navigation satellite system (GLONASS), Galileo satellite navigation system (Galileo), and BeiDou navigation satellite system (BDS) from the P387 station of the U.S. Plate Boundary Observatory (PBO) are analyzed. A Lomb–Scargle periodogram (LSP) spectrum analysis is used to compare the difference in reflector height between the snow-free and snow surfaces in order to retrieve the snow depth, which is compared with the PBO snow depth. First, the different frequency retrieval results of the multi-GNSS system are analyzed. Then, the retrieval accuracy of the different GNSS systems is analyzed through multi-frequency mean fusion. Finally, the joint retrieval accuracy of the multi-GNSS system is analyzed through mean fusion. The experimental shows that the retrieval results of different frequencies of the multi-GNSS system have a strong correlation with the PBO snow depth, and that the accuracy is better than 10 cm. The multi-frequency mean fusion of different GNSS systems can effectively improve the retrieval accuracy, which is better than 7 cm. The joint retrieval accuracy of the multi-GNSS system is further improved, with a correlation coefficient (R) between the retrieval snow depth and the PBO snow depth of 0.99, and the accuracy is better than 3 cm. Therefore, using multi-GNSS and multi-frequency data to retrieve the snow depth has a good accuracy and feasibility.
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Rignot, Eric, and Mark R. Drinkwater. "Winter Sea-ice mapping from multi-parameter synthetic-aperture radar data." Journal of Glaciology 40, no. 134 (1994): 31–45. http://dx.doi.org/10.1017/s0022143000003774.
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AbstractThe limitations of current and immediate future single-frequency, single-polarization, space-borne SARs for winter sea-ice mapping are quantitatively examined, and improvements are suggested by combining frequencies and polarizations. Ice-type maps are generated using multi-channel, air-borne SAR observations of winter sea ice in the Beaufort Sea to identify six ice conditions: (1) multi-year sea ice; (2) compressed first-year ice; (3) first-year rubble and ridges; (4) first-year rough ice; (5) first-year smooth ice; and (6) first-year thin ice. At a single polarization, C- (λ = 5.6 cm) and L- (λ = 24 cm) band frequencies yield a classification accuracy of 67 and 71%, because C-band confuses multi-year ice and compressed, rough, thick first-year ice surrounding multi-year ice floes, and L-band confuses multi-year ice and deformed first-year ice. Combining C- and L-band improves classification accuracy by 20%. Adding a second polarization at one frequency only improves classification accuracy by 10–14% and separates thin ice and calm open water. Under similar winter-ice conditions, ERS-1 (Cvv) and Radarsat (CHH) would overestimate the multi-year ice fraction by 15% but correctly map the spatial variability of ice thickness; J-ERS-1 (LHH) would perform poorly;and J-ERS-1 combined with ERS-1 or Radarsat would yield reliable estimates of the old, thick, first-year and thin-ice fractions, and of the spatial distribution of ridges. With two polarizations, future single-frequency space-borne SARs could improve our current capability to discriminate thinner ice types.
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Rignot, Eric, and Mark R. Drinkwater. "Winter Sea-ice mapping from multi-parameter synthetic-aperture radar data." Journal of Glaciology 40, no. 134 (1994): 31–45. http://dx.doi.org/10.3189/s0022143000003774.
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AbstractThe limitations of current and immediate future single-frequency, single-polarization, space-borne SARs for winter sea-ice mapping are quantitatively examined, and improvements are suggested by combining frequencies and polarizations. Ice-type maps are generated using multi-channel, air-borne SAR observations of winter sea ice in the Beaufort Sea to identify six ice conditions: (1) multi-year sea ice; (2) compressed first-year ice; (3) first-year rubble and ridges; (4) first-year rough ice; (5) first-year smooth ice; and (6) first-year thin ice. At a single polarization, C- (λ = 5.6 cm) and L- (λ = 24 cm) band frequencies yield a classification accuracy of 67 and 71%, because C-band confuses multi-year ice and compressed, rough, thick first-year ice surrounding multi-year ice floes, and L-band confuses multi-year ice and deformed first-year ice. Combining C- and L-band improves classification accuracy by 20%. Adding a second polarization at one frequency only improves classification accuracy by 10–14% and separates thin ice and calm open water. Under similar winter-ice conditions, ERS-1 (Cvv) and Radarsat (CHH) would overestimate the multi-year ice fraction by 15% but correctly map the spatial variability of ice thickness; J-ERS-1 (LHH) would perform poorly;and J-ERS-1 combined with ERS-1 or Radarsat would yield reliable estimates of the old, thick, first-year and thin-ice fractions, and of the spatial distribution of ridges. With two polarizations, future single-frequency space-borne SARs could improve our current capability to discriminate thinner ice types.
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Yao, Zhiying, Yuanyuan Zhao, Hengbin Wang, Hongdong Li, Xinqun Yuan, Tianwei Ren, Le Yu, Zhe Liu, Xiaodong Zhang, and Shaoming Li. "Comparison and Assessment of Data Sources with Different Spatial and Temporal Resolution for Efficiency Orchard Mapping: Case Studies in Five Grape-Growing Regions." Remote Sensing 15, no. 3 (January 22, 2023): 655. http://dx.doi.org/10.3390/rs15030655.
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As one of the most important agricultural production types in the world, orchards have high economic, ecological, and cultural value, so the accurate and timely mapping of orchards is highly demanded for many applications. Selecting a remote-sensing (RS) data source is a critical step in efficient orchard mapping, and it is hard to have a RS image with both rich temporal and spatial information. A trade-off between spatial and temporal resolution must be made. Taking grape-growing regions as an example, we tested imagery at different spatial and temporal resolutions as classification inputs (including from Worldview-2, Landsat-8, and Sentinel-2) and compared and assessed their orchard-mapping performance using the same classifier of random forest. Our results showed that the overall accuracies improved from 0.6 to 0.8 as the spatial resolution of the input images increased from 58.86 m to 0.46 m (simulated from Worldview-2 imagery). The overall accuracy improved from 0.7 to 0.86 when the number of images used for classification was increased from 2 to 20 (Landsat-8) or approximately 60 (Sentinel-2) in one year. The marginal benefit of increasing the level of details (LoD) of temporal features on accuracy is higher than that of spatial features, indicating that the classification ability of temporal information is higher than that of spatial information. The highest accuracy of using a very high-resolution (VHR) image can be exceeded only by using four to five medium-resolution multi-temporal images, or even two to three growing season images with the same classifier. Combining the spatial and temporal features from multi-source data can improve the overall accuracies by 5% to 7% compared to using only temporal features. It can also compensate for the accuracy loss caused by missing data or low-quality images in single-source input. Although selecting multi-source data can obtain the best accuracy, selecting single-source data can improve computational efficiency and at the same time obtain an acceptable accuracy. This study provides practical guidance on selecting data at various spatial and temporal resolutions for the efficient mapping of other types of annual crops or orchards.
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Carl, Gudrun, Sam Levin, and Ingolf Kühn. "spind: an R Package to Account for Spatial Autocorrelation in the Analysis of Lattice Data." Biodiversity Data Journal 6 (February 28, 2018): e20760. http://dx.doi.org/10.3897/bdj.6.e20760.
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spind is an R package aiming to provide a useful toolkit to account for spatial dependence in the analysis of lattice data. Grid-based data sets in spatial modelling often exhibit spatial dependence, i.e. values sampled at nearby locations are more similar than those sampled further apart. spind methods, described here, take this kind of two-dimensional dependence into account and are sensitive to its variation across different spatial scales. Methods presented to account for spatial autocorrelation are based on the two fundamentally different approaches of generalised estimating equations as well as wavelet-revised methods. Both methods are extensions to generalised linear models. spind also provides functions for multi-model inference and scaling by wavelet multiresolution regression. Since model evaluation is essential for assessing prediction accuracy in species distribution modelling, spind additionally supplies users with spatial accuracy measures, i.e. measures that are sensitive to the spatial arrangement of the predictions.
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Kozoderov, V. V., and V. D. Egorov. "Pattern recognition of forest canopy using the airborne hyperspectral data and multi-bands high spatial resolution satellite sensor worldview-2 data. A results comparison and accuracy estimation." Исследования Земли из Космоса, no. 6 (December 21, 2019): 89–102. http://dx.doi.org/10.31857/s0205-96142019689-102.
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Pattern recognition of forest surface from remote sensing data: using the airborne hyperspectral data and using multi-bands high spatial resolution satellite sensor WorldView‑2 data are investigated. The early proposed method and standard QDA method for calculations were used. A comparison of calculations results were conducted. A recognition calculation accuracy range for airborne and satellite remote sensing data for three forest surface fragments for different created data bases for recognition system has been assessed. Some opportunities of automatic data preparing of created system were displayed. Some special features of pattern recognition of forest surfaces from hyperspectral airborne data and from multi-bands high spatial resolution satellite data were discussed.
Dissertations / Theses on the topic "Multi-accuracy spatial data"
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MIGLIORINI, Sara. "Supporting Distributed Geo-Processing: A Framework for Managing Multi-Accuracy Spatial Data." Doctoral thesis, 2012. http://hdl.handle.net/11562/397936.
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Negli ultimi anni molti paesi hanno sviluppato un'infrastruttura tecnologica al fine di gestire i propri dati geografici (Spatial Data Infrastructure, SDI). Tali infrastrutture rechiedono nuove ed efficati metodologie per integrare continuamente dati che provengoono da sorgenti diverse e sono caratterizzati da diversi livelli di qualità. Questo bisogno è riconosciuto in letteratura ed è noto come problema di integrazione del dato (data integration) o fusione di informazioni (information fusion). Un aspetto peculiare dell'integrazione del dato geografico riguarda il matching e l'allineamento degli oggetti geometrici. I metodi esistenti solitamente eseguono l'integrazione semplicemente allineando il database meno accurato con quello più accurato, assumendo che il secondo contenga sempre una rappresentazione migliore delle geometrie rilevate. Seguendo questo approccio, gli oggetti geografici sono combinati assieme in una maniera non ottimale, causando distorsioni che potenzialmente riducono la qualità complessiva del database finale. Questa tesi si occupa del problema dell'integrazione del dato spaziale all'interno di una SDI fortemente strutturata, in cui i membri hanno preventivamente aderito ad uno schema globale comune, pertanto si focalizza sul problema dell'integrazione geometrica, assumendo che precedenti operazioni di integrazione sullo schema siano già state eseguire. In particulare, la tesi inizia proponendo un modello per la rappresentazione dell'informazione spaziale caratterizzata da differenti livelli di qualità, quindi definisce un processo di integrazione che tiene conto dell'accuratezza delle posizioni contenute in entrambi i database coinvoilti. La tecnica di integrazione proposta rappresenta la base per un framework capace di supportare il processamento distributo di dati geografici (geo-processing) nel contesto di una SDI. Il problema di implementare tale computazione distribuita e di lunga durata è trattato anche da un punto di vista pratico attraverso la valutazione dell'applicabilità delle tecnologie di workflow esistenti. Tale valutazione ha portato alla definizione di una soluzione software ideale, le cui caratteristiche sono discusse negli ultimi capitoli, considerando come caso di studio il design del processo di integrazione proposto.In the last years many countries have developed a Spatial Data Infrastructure (SDI) to manage their geographical information. Large SDIs require new effective techniques to continuously integrate spatial data coming from different sources and characterized by different quality levels. This need is recognized in the scientific literature and is known as data integration or information fusion problem. A specific aspect of spatial data integration concerns the matching and alignment of object geometries. Existing methods mainly perform the integration by simply aligning the less accurate database with the more accurate one, assuming that the latter always contains a better representation of the relevant geometries. Following this approach, spatial entities are merged together in a sub-optimal manner, causing distortions that potentially reduce the overall database quality. This thesis deals with the problem of spatial data integration in a highly-coupled SDI where members have already adhered to a common global schema, hence it focuses on the geometric integration problem assuming that some schema matching operations have already been performed. In particular, the thesis initially proposes a model for representing spatial data together with their quality characteristics, producing a multi-accuracy spatial database, then it defines a novel integration process that takes care of the different positional accuracies of the involved source databases. The main goal of such process is to preserve coherence and consistency of the integrated data and when possible enhancing its accuracy. The proposed multi-accuracy spatial data model and the related integration technique represent the basis for a framework able to support distributed geo-processing in a SDI context. The problem of implementing such long-running distributed computations is also treated from a practical perspective by evaluating the applicability of existing workflow technologies. This evaluation leads to the definition of an ideal software solution, whose characteristics are discussed in the last chapters by considering the design of the proposed integration process as a motivating example.
Book chapters on the topic "Multi-accuracy spatial data"
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Ikokou, Guy Blanchard, and Julian Lloyd Smit. "Optimizing the Selection of Spatial and Non-spatial Data for Higher Accuracy Multi-scale Classification of Urban Environments." In Southern Space Studies, 161–69. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16016-6_14.
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Li, Dong, and Xiaobo Peng. "Research on EEG Feature Extraction and Recognition Method of Lower Limb Motor Imagery." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 1209–18. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_121.
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AbstractAiming at the problems of difficult signal acquisition, low signal-to-noise ratio and poor classification accuracy of BCI technology, based on the theory of EEG, this paper designs a leg raising EEG experiment of lower limb motor imagery and collects EEG signal data from 20 subjects to improve the accuracy of classification and recognition The process of feature extraction and classification recognition is explored, and a multi domain fusion method is proposed for EEG signal feature extraction from time domain, frequency domain, time-frequency domain and spatial domain. At the same time, bagging and gradient boosting ensemble learning algorithms are applied to EEG signal classification and recognition, and multi domain fusion features are tested by constructing different classifiers, The final classification accuracy reaches 87.8% and 93%, which is better than the traditional SVM classification method.
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Morota, Gota, Diego Jarquin, Malachy T. Campbell, and Hiroyoshi Iwata. "Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data." In Methods in Molecular Biology, 269–96. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2537-8_21.
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AbstractThe advent of plant phenomics, coupled with the wealth of genotypic data generated by next-generation sequencing technologies, provides exciting new resources for investigations into and improvement of complex traits. However, these new technologies also bring new challenges in quantitative genetics, namely, a need for the development of robust frameworks that can accommodate these high-dimensional data. In this chapter, we describe methods for the statistical analysis of high-throughput phenotyping (HTP) data with the goal of enhancing the prediction accuracy of genomic selection (GS). Following the Introduction in Sec. 1, Sec. 2 discusses field-based HTP, including the use of unoccupied aerial vehicles and light detection and ranging, as well as how we can achieve increased genetic gain by utilizing image data derived from HTP. Section 3 considers extending commonly used GS models to integrate HTP data as covariates associated with the principal trait response, such as yield. Particular focus is placed on single-trait, multi-trait, and genotype by environment interaction models. One unique aspect of HTP data is that phenomics platforms often produce large-scale data with high spatial and temporal resolution for capturing dynamic growth, development, and stress responses. Section 4 discusses the utility of a random regression model for performing longitudinal modeling. The chapter concludes with a discussion of some standing issues.
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Sun, Xinyao, Anup Basu, and Irene Cheng. "Multi-Sensor Motion Fusion Using Deep Neural Network Learning." In Deep Learning and Neural Networks, 568–86. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch032.
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Hand pose estimation for a continuous sequence has been an important topic not only in computer vision but also human-computer-interaction. Exploring the feasibility to use hand gestures to replace input devices, e.g., mouse, keyboard, joy-stick and touch screen, has attracted increasing attention from academic and industrial researchers. The fast advancement of hand pose estimation techniques is complemented by the rapid development of smart sensors technology such as Kinect and Leap. We introduce a hand pose estimation multi-sensor system. Two tracking models are proposed based on Deep (Recurrent) Neural Network (DRNN) architecture. Data captured from different sensors are analyzed and fused to produce an optimal hand pose sequence. Experimental results show that our models outperform previous methods with better accuracy, meeting real-time application requirement. Performance comparisons between DNN and DRNN, spatial and spatial-temporal features, and single- and dual- sensors, are also presented.
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Misbari, S., J. I. A. Gisen, and M. Hashim. "DETECTION AND QUANTIFICATION OF SUBMERGED SEAGRASS TOTAL ABOVEGROUND BIOMASS CHANGES IN TINGGI ISLAND, JOHOR USING REMOTE SENSING DATA." In Construction Engineering and Management. PENERBIT UNIVERSITI MALAYSIA PAHANG, 2022. http://dx.doi.org/10.15282/cem.1.04.2022.02.05.
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Tinggi Island area is gazetted as Johor Marine Park while unpredictable natural phenomenon claimed as a primary threat that caused reduction of submerged seagrass total aboveground biomass (SSTAGB). This study aims to (a) detect and quantify SSTAGB in clear water of the Tinggi Island area using satellite data, and (b) assess SSTAGB changes in 2009 and 2014. Algorithm of Bottom Reflectance Index is implemented on Landsat 8 OLI image of 2009 and 2014 to detect and study spatial distribution of multi-species submerged seagrass. Field data sampling was conducted to validate the classified satellite image. A series of quadrat sampling of 0.5mx0.5m was used to quantify ground-based SSTAGB. The result found that the seagrass area and SSTAGB around the island are rigid and remarkably consistent over a 5-year interval. A strong association between ground-based SSTAGB and satellite-based SSTAGB shows that the BRI model is significantly satisfied to be implemented on moderate resolution of satellite data with overall accuracy of >70% and >0.65 of kappa statistic.
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He, Xing, Rui Huang, Minqi Yu, Wenwei Zeng, and Suihan Zhang. "Multi-State Recognition Method of Substation Switchgear Based on Image Enhancement and Deep Learning." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde221211.
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s. Since the current substation robot inspection process exists in the high-voltage switchgear status recognition is highly susceptible to the influence of complex environments such as low image contrast, light clutter interference, and blurred reading status details, this paper proposes an image enhancement and deep learning based substation switchgear state recognition method. A multi-scale Retinex-based image enhancement is proposed to enhance the adaptability of outdoor switchgear images to light changes; improve the YOLOx target detection network to introduces a lightweight ECA attention mechanism without dimensionality reduction based on the original YOLOx model’s backbone network CSPDarknet, allowing the model to learn classification features while also focusing on learning spatial features. The experimental results show that the improved network can accurately identify the boundary information of anomalies, and the quality of its prediction results will not be reduced for noise-containing data, and the network shows strong generalization, robustness, accuracy and rapidity, providing certain conditions for realizing substation equipment condition monitoring.
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"Pacific Salmon Environmental and Life History Models: Advancing Science for Sustainable Salmon in the Future." In Pacific Salmon Environmental and Life History Models: Advancing Science for Sustainable Salmon in the Future, edited by Randall M. Petermrman, Brian J. Pyper, Franz J. Mueter, Steven L. Haeseker, Zhenming Su, and Brigitte Dorner. American Fisheries Society, 2009. http://dx.doi.org/10.47886/9781934874097.ch8.
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<em>Abstract.</em>—Past attempts to improve population models of Pacific salmon <em>Oncorhynchus </em>spp. by adding indices of freshwater or marine conditions have shown mixed success. To increase chances that such models will remain reliable over the long term, we suggest adding only environmental covariates that have a spatial scale of positive correlation among monitoring locations similar to, or greater than, that of the salmon variables that scientists are trying to explain. To illustrate this approach, we analyzed spawner and recruit data for 120 populations (stocks) of pink <em>O. gorbuscha</em>, chum <em>O. keta</em>, and sockeye <em>O. nerka </em>salmon from Washington, British Columbia, and Alaska. Salmon productivity of a given species was positively correlated across stocks at a spatial scale of about 500–800 km. Compared to upwelling and sea-surface salinity, summer sea-surface temperature (SST) showed a more appropriate spatial scale of positive covariation for explaining variation in salmon productivity, and was a significant explanatory variable when added to both single-stock and multi-stock spawner-recruit models. This result suggests that future models of these salmon populations should possibly include stock-specific, summer SST. To further explore our understanding of salmon population dynamics, we developed 24 alternative stock–recruitment models. We compared these models in three ways: (1) their fit to all past data, (2) their ability to forecast recruitment, and (3) their performance inside an “operating model,” which included components for dynamics of the natural ecological system, stock assessments based on simulated sampling of data, regulation-setting based on those assessments, and variation in implementing those regulations (reflecting noncompliance or other sources of outcome uncertainty). We also compared single-stock models with multi-stock models (meta-analyses). The latter led to more precise estimates of the effects of SST on log<sub>e</sub>(recruits/spawner) and greater accuracy of preseason forecasts for some stocks. Analyses with the operating model show that reducing outcome uncertainty should be a top management priority.
Conference papers on the topic "Multi-accuracy spatial data"
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Machado, Vanessa Lago, Ronaldo dos Santos Mello, and Vânia Bogorny. "On Generating Representative Data for Multiple Aspects Trajectory Data." In Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sbbd_estendido.2022.21850.
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Trajectory data mining and analysis tasks have been widely studied in recent years. These tasks are complex due to the large volume of data generated and its heterogeneity. A solution to minimize these problems is the summarization of these data, aiming to generate representative data. Few works in the literature address these solutions, and none were found that consider all dimensions of a trajectory (spatial, temporal and unlimited semantic aspects), analyzing the peculiarities and singularities of each aspect. This doctoral thesis proposes a method based on a spatial grid for summarizing multi-aspect trajectories, called MAT-SG. Its main contributions are: (i) segmentation of trajectories in a spatial grid according to the dispersion of points; (ii) from a set of input trajectories, a representative trajectory is generated as a sequence of representative points with representative values for each dimension, considering the particularities of each type of aspect. An example demonstrates the potential of the proposal, being evaluated the volume reduction and the accuracy of the summarization.
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Subramanian, Amirthaganesh, Lauren K. Yee, Jaikrishnan R. Kadambi, Mark P. Wernet, and Hiroaki Harasaki. "Application of High Resolution PIV Processing in Flow Through Mechanical Heart Valve." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/bed-23167.
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Abstract Particle image velocimetry (PIV) processing techniques, Multi Pass Correlation (MPC) and Particle Tracking Velocimetry (PTV), are applied to images obtained from a bileaflet mechanical heart valve study to obtain velocity measurements with increased spatial resolution and accuracy. Using subregions of 32 pixels by 64 pixels, a spatial resolution of 0.23 mm in x and 0.46 mm in y is obtained, as compared to 0.46 mm in both directions for traditional PIV processing. When MPC and PTV are not utilized, spatial resolution can only be increased by decreasing the subregion size and time between images in an image pair, which sacrifices accuracy. High accuracy can only be obtained by increasing the subregion size and the time between pulses, which reduces spatial resolution. MPC and PTV allow both high spatial resolution and accuracy by altering the processing method as opposed to how the data is produced conventionally.
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Ren, Jie, and Hui Wang. "Surface Variation Modeling by Fusing Surface Measurement Data With Multiple Manufacturing Process Variables." In ASME 2016 11th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/msec2016-8717.
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Controlling surface shape variations plays a key role in high-precision manufacturing. Most manufacturing plants rely on a number of multi-resolution measurements on manufactured surfaces to evaluate surface shapes and resultant quality. Conventional research on surface shape modeling focused on interpolation and extrapolation of spatial data using sampled measurements based on presumed spatial relationship over entire surface locations. However, the prediction accuracy is heavily restricted by the density of sampled measurements, preventing cost-effective evaluation of surface shape in high precision. New opportunities emerge for cost-effective high-precision surface manufacturing when the industry begins to extensively collect in-plant process information. This paper explores the opportunity by investigating strategies for fusing surface measurement data with multiple process variables. The fusion is achieved by characterizing the relationships between surface height and process variables using (1) linear regression based co-Kriging and (2) fuzzy if-then rules as well as considering spatial correlations. Under (3) Bayesian sequential updating frameworks, a generic surface variation model is updated sequentially using different process information. Case studies are conducted for comparisons and demonstrate the advantages of the fuzzy inference based spatial model.
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Romero, David A., Cristina Amon, and Susan Finger. "Modeling Time-Dependent Systems Using Multi-Stage Bayesian Surrogates." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-55049.
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Multi-Stage Bayesian Surrogate Models (MBSM) are meta-models, constructed using data obtained from different sources, which have the ability to integrate information and responses with different levels of accuracy. In applications of surrogate models for time-dependent systems, the data obtained from physical or computational experiments is usually a sequence of response values over time, measured for different combinations of design parameters. For such applications, the traditional MBSM approach is impractical to incorporate all the observed data in a single model of the system, mainly due to the prohibitive computational effort involved. In this paper, we propose a framework for building surrogate models for time-dependent systems, based on the cokriging technique. The proposed framework regards the observations as a set of time-correlated spatial processes, with a stationary, separable cross-covariance structure of known functional form. Results show that for time-dependent systems, the proposed methodology outperforms joint space-time models built with the traditional MBSM approach both in terms of accuracy and computational effort.
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Mackay, Ed, AbuBakr Bahaj, Chris Retzler, and Peter Challenor. "Wave Energy Resource Assessment Using Satellite Altimeter Data." In ASME 2008 27th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2008. http://dx.doi.org/10.1115/omae2008-57976.
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The use of altimeter measurements of significant wave height and energy period for quantifying wave energy resource is investigated. A new algorithm for calculating wave period from altimeter data, developed by the authors in previous work, is used to estimate the power generated by the Pelamis wave energy converter and compared to estimates from collocated buoy data. In offshore locations accurate estimates of monthly and annual mean power can be achieved by combining measurements from six altimeter missions. Furthermore, by averaging along sections of the altimeter ground track, we demonstrate that it is possible to gauge the spatial variability in nearshore areas, with a resolution of the order of 10 km. Although measurements along individual tracks are temporally sparse, with TOPEX/Poseidon and Jason on a 10 day repeat orbit, GFO 17 days, and ERS-2 and ENVISAT 35 days, the long record of altimeter measurements means that multi-year mean power from single tracks are of a useful accuracy.
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Karg Bulnes, Fernando, Kyle R. Gluesenkamp, and Joseph Rendall. "Comparison of Plug Flow and Multi-Node Stratified Tank Modeling Approaches Regarding Computational Efficiency and Accuracy." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23369.
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Abstract Residential water heaters contain water stratified by temperature-driven density differences. This implies that a water tank can reach a state in which the top and bottom sections have different temperatures, unless mixing happens. A high degree of thermal stratification can improve the efficiency of some water heaters, by saving the amount of energy required for the heat-up process. Studies of stratification became popular in the 1970s and it remains an active research topic today. The research has led to the development of different models and techniques to better predict and define a stratified tanks behavior. By comparing these models and techniques used previously to describe thermal stratification, the phenomenon could be better understood, exploited, and used to increase efficiency and thermal energy capacity in modern water tanks. From the existing models, we found the one-dimensional standard plug-flow and a multi node model to be appropriate for analyzing the processes of the heat up and cool-down in a water tank. These two models are based on energy balances. This work involved comparing the accuracy and computational effort needed to implement these models. To assess accuracy, we compared both types of existing models to experimental data (also collected in this work) which included a heat up process using an external heat pump. This external process included a layering process that has an eddy diffusivity at five times the rate of thermal diffusion. For this project, we implemented the models in MATLAB, the multi-paradigm numerical computing environment. We quantified model accuracy using the root mean squared error between modeled data and experimental data for six measured tank temperatures. Comparing the accuracy and the computational time taken to run the simulation provides a method to contrast the performance of each model and a way to rate it. The multi node model was run using from 6 to 96 spatial nodes; the plug flow model was run using 1 to 0.001 °C temperature bin sizes. Additionally, timesteps were varied from 4 to 236 s. The results quantify the tradeoff between accuracy and computational time, providing guidance for simulations to intelligently select the best model type and simulation parameters. This research can be used to validate the pre-existing models and possibly improve the modern water tank.
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Brown, Jeff S., Evan H. Martin, and Arno Keinonen. "Ice Management Numerical Modeling and Modern Data Sources." In SNAME 10th International Conference and Exhibition on Performance of Ships and Structures in Ice. SNAME, 2012. http://dx.doi.org/10.5957/icetech-2012-136.
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Numerical modeling is a cost effective planning tool for both operational feasibility and design studies. This is particularly true in the Arctic offshore industry where operational costs are high. Ice management, the defense of platforms or floaters via systematic icebreaking, is a useful tool to ensure ice loads are kept within manageable limits. The availability of new data types, departing in detail from traditional ice observer records allows more specific modeling to occur in support of conceptual designs. Issues with applying these data sources have been identified, and are generally caused by the assumptions made during calibration of existing icebreaker performance models. A methodology to extract general ice severity statistics from spatial Upward Looking Sonar (ULS) data is presented to allow for a direct application within existing performance models. Identification of severe features (first year ridges and multi-year floes) is also discussed. In addition, a method for deriving floe size distributions from analytical ice management predictions is briefly described. Finally recommendations for of future work in terms of increasing the reliability and accuracy of icebreaker performance and ice management modeling are made.
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KHAJWAL, ASIM B., CHIH-SHEN CHENG, and ARASH NOSHADRAVAN. "MULTI-VIEW DEEP LEARNING FOR RELIABLE POST-DISASTER DAMAGE CLASSIFICATION." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36310.
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This study aims to enable more reliable automated post-disaster building damage classification using artificial intelligence (AI) and multi-view imagery. The current practices and research efforts in adopting AI for post-disaster damage assessment are generally (a) qualitative, lacking refined classification of building damage levels based on standard damage scales, and (b) trained based on aerial or satellite imagery with limited views, which, although indicative, are not completely descriptive of the damage scale. To enable more accurate and reliable automated quantification of damage levels, the present study proposes the use of more comprehensive visual data in the form of multiple ground and aerial views of the buildings. To have such a spatially-aware damage prediction model, a Multi-view Convolution Neural Network (MV-CNN) architecture is used that combines the information from different views of a damaged building. This spatial 3D context damage information will result in more accurate identification of damages and reliable quantification of damage levels. The proposed model is trained and validated on reconnaissance visual dataset containing expertlabeled, geotagged images of the inspected buildings following hurricane Harvey. The developed model demonstrates reasonably good accuracy in predicting the damage levels and can be used to support more informed and reliable AI-assisted disaster management practices.
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Korb, C. Laurence, Bruce M. Gentry, and S. Xingfu Li. "Edge Technique Lidar Measurement of the Atmospheric Wind Field." In Optical Remote Sensing of the Atmosphere. Washington, D.C.: Optica Publishing Group, 1997. http://dx.doi.org/10.1364/orsa.1997.otua.5.
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In this paper we present high spatial resolution lidar wind measurements with the edge technique using a ground based lidar system we have recently developed. The edge technique [1],[2] allows measurement of small frequency shifts such as the Doppler shift of an atmospheric backscattered signal from a pulsed laser. This yields a direct measurement of the wind along the line of sight of the laser beam. The edge technique can be used for high spatial resolution, high accuracy ground and airborne wind measurement as well as high accuracy spaceborne wind measurement. We show that line of sight wind profiles with a vertical resolution of 22 m have a standard deviation of 0.4 m/s for a 10 shot average. Validation data using independent rawinsonde and optical theodolite measurements show good agreement with the lidar wind measurements at the 1 m/s level. The instrument noise level for the lidar data is 0.11 m/s. This is a unique capability and provides valuable information for studies of turbulent processes in the lower atmosphere. This capability could also be used for high sensitivity detection of wind shear and microbursts in the vicinity of airports. Simulations show the edge technique can be used from satellites to obtain global wind measurements with an accuracy of the order of 1 m/s and a vertical resolution of 1 km through the troposphere. Such a system could make eyesafe wind measurements using well developed diode pumped solid state laser technology at 1.06 µm. Multi-pulse averaging would provide a spatially representative wind measurement. In addition, at ultraviolet wavelengths the Rayleigh backscatter could be used and would provide a large signal but with reduced measurement sensitivity.
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Nakajima, Kenta, and Michael King. "Development and Application of Fast Simulation Based on the PSS Pressure as a Spatial Coordinate." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206085-ms.
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Abstract Recent studies have shown the utility of the Fast Marching Method and the Diffusive Time of Flight for the rapid simulation and analysis of Unconventional reservoirs, where the time scale for pressure transients are long and field developments are dominated by single well performance. We show that similar fast simulation and multi-well modeling approaches can be developed utilizing the PSS pressure as a spatial coordinate, providing an extension to both Conventional and Unconventional reservoir analysis. We reformulate the multi-dimensional multi-phase flow equations using the PSS pressure drop as a spatial coordinate. Properties are obtained by coarsening and upscaling a fine scale 3D reservoir model, and are then used to obtain fast single well simulation models. We also develop new 1D solutions to the Eikonal equation that are aligned with the PSS discretization, which better represent superposition and finite sized boundary effects than the original 3D Eikonal equation. These solutions allow the use of superposition to extend the single well results to multiple wells. The new solutions to the Eikonal equation more accurately represent multi-fracture interference for a horizontal MTFW well, the effects of strong heterogeneity, and finite reservoir extent than those obtained by the Fast Marching Method. The new methodologies are validated against a series of increasingly heterogeneous synthetic examples, with vertical and horizontal wells. We find that the results are systematically more accurate than those based upon the Diffusive Time of Flight, especially as the wells are placed closer to the reservoir boundary or as heterogeneity increases. The approach is applied to the Brugge benchmark study. We consider the history matching stage of the study and utilize the multi-well fast modeling approach to determine the rank quality of the 100+ static realizations provided in the benchmark dataset against historical data. The multi-well calculation uses superposition to obtain a direct calculation of the interaction of the rates and pressures of the wells without the need to explicitly solve flow equations within the reservoir model. The ranked realizations are then compared against full field simulation to demonstrate the significant reduction in simulation cost and the corresponding ability to explore the subsurface uncertainty more extensively. We demonstrate two completely new methods for rapid reservoir analysis, based upon the use of the PSS pressure as a spatial coordinate. The first approach demonstrates the utility of rapid single well flow simulation, with improved accuracy compared to the use of the Diffusive Time of Flight. We are also able to reformulate and solve the Eikonal equation in these coordinates, giving a rapid analytic method of transient flow analysis for both single and multi-well modeling.