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1
Wang, Zhao Hong. "Normal Distribution Data Generating Method Based on Cloud Model." Advanced Materials Research 171-172 (December 2010): 385–88. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.385.
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The similar normal distribution is used wildly in the natural science and social science, fuzzy membership degree function which is accurately established seriously reduces the forecast accuracy of such data. Cloud model compare randomness and fuzziness organically, it reveal the relevance between randomness and fuzziness with digital expectations, entropy and hyper entropy, forecast algorithm based on normal cloud model relaxed the requirements of a normal distribution prerequisite and replaced the accurate membership degree function with the membership degree distribution expectation function, it is more easier and simpler than the joint distribution, Comparative experiment showed it is more general, can complete the data forecast accurately and directly.
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Sung-Shik Koh, H. Hama, and T. T. Zin. "Accurate Estimation of Missing Data under Noise Distribution." IEEE Transactions on Consumer Electronics 52, no. 2 (May 2006): 528–35. http://dx.doi.org/10.1109/tce.2006.1649675.
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Trombik, J., and T. Hlásny. "Free European data on forest distribution: overview and evaluation." Journal of Forest Science 59, No. 11 (November 29, 2013): 447–57. http://dx.doi.org/10.17221/58/2013-jfs.
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A growing need for the evaluation of prospects and sustainability of forest resources calls for the availability of harmonized data on forest distribution. We described and evaluated nine datasets providing such information: Corine LandCover, four European forest maps and four tree species distribution maps. Apart from providing a condensed overview of these datasets, we focused on the match between selected forest maps and forest management plans (FMPs) of Slovakia, which can be thought of as highly accurate information on forest distribution. The degree of match between forest and species area, within 306 forest administrative districts of Slovakia, was used as an indicator of accuracy. In addition, the match between the total forest and species area in Slovakia, given by FMPs and by evaluated datasets, was addressed. We found a high degree of match for the datasets on forest distribution (R-square 0.77–0.93, depending on the dataset), as well as strong agreement in total forest area (± 5%). Both indicators are worse in the case of forest type evaluation (coniferous and broadleaved). Poor results were obtained for tree species maps, which under- or overestimated species areas by tens of per cent, although differences were highly variable among species. The obtained results are valid mainly for temperate forests.
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Trumbo, D. R., A. A. Burgett, and J. H. Knouft. "Testing climate-based species distribution models with recent field surveys of pond-breeding amphibians in eastern Missouri." Canadian Journal of Zoology 89, no. 11 (November 2011): 1074–83. http://dx.doi.org/10.1139/z11-083.
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Species distribution models (SDMs) have become an important tool for ecologists by providing the ability to predict the distributions of organisms based on species niche parameters and available habitat across broad geographic areas. However, investigation of the appropriate extent of environmental data needed to make accurate predictions has received limited attention. We investigate whether SDMs developed with regional climate and species locality data (i.e., within Missouri, USA) produce more accurate predictions of species occurrences than models developed with data from across an entire species range. To test the accuracy of the model predictions, field surveys were performed in 2007 and 2008 at 103 study ponds for eight amphibian study species. Models developed using data from across the entire species range did not accurately predict the occurrences of any study species. However, models developed using data only from Missouri produced accurate predictions for four study species, all of which are near the edge of their geographic ranges within the study area. These results suggest that species distribution modeling with regionally focused data may be preferable for local ecological and conservation purposes, and that climate factors may be more important for determining species distributions at the edge of their geographic ranges.
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Liu, Yu, Xiaoping Wang, and Jiaxin Qian. "Crop distribution extraction based on Sentinel data." E3S Web of Conferences 252 (2021): 02081. http://dx.doi.org/10.1051/e3sconf/202125202081.
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Remote sensing identification and classification of crops is the use of remote sensing for estimating crop planting area of timely and accurate monitoring of crop growth and plant diseases and insect pests in advance to make the product output to estimate the key and premise of the study using Sentinel-1 and Sentinel-2 satellite, by random forest algorithm, the traditional optical wavelengths and vegetation index The backward scattering field of red edge information and radar information in feature selection and feature classification, including winter wheat summer corn orchard woodland town water and bare land set three controls, such as the first group contains radar time characteristics, the characteristics of the second control group contains red edge long, the third group includes traditional vegetation index for phase characteristics, analyzed the different classification accuracy. The results from the confusion matrix show that the red edge band edge after index and the radar scattering information to join the crop classification accuracy is improved effectively. Sentinel optical and radar satellites with a time resolution of 5–6 days have great potential for crop monitoring research.
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Xintao, Xia, Chang Zhen, Zhang Lijun, and Yang Xiaowei. "Estimation on Reliability Models of Bearing Failure Data." Mathematical Problems in Engineering 2018 (2018): 1–21. http://dx.doi.org/10.1155/2018/6189527.
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The failure data of bearing products is random and discrete and shows evident uncertainty. Is it accurate and reliable to use Weibull distribution to represent the failure model of product? The Weibull distribution, log-normal distribution, and an improved maximum entropy probability distribution were compared and analyzed to find an optimum and precise reliability analysis model. By utilizing computer simulation technology and k-s hypothesis testing, the feasibility of three models was verified, and the reliability of different models obtained via practical bearing failure data was compared and analyzed. The research indicates that the reliability model of two-parameter Weibull distribution does not apply to all situations, and sometimes, two-parameter log-normal distribution model is more precise and feasible; compared to three-parameter log-normal distribution model, the three-parameter Weibull distribution manifests better accuracy but still does not apply to all cases, while the novel proposed model of improved maximum entropy probability distribution fits not only all kinds of known distributions but also poor information issues with unknown probability distribution, prior information, or trends, so it is an ideal reliability analysis model with least error at present.
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Lin, Zhidi, Dongliang Duan, Qi Yang, Xuemin Hong, Xiang Cheng, Liuqing Yang, and Shuguang Cui. "Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources." Energies 13, no. 1 (January 6, 2020): 275. http://dx.doi.org/10.3390/en13010275.
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The integration of Distributed Energy Resources (DERs) introduces a non-conventional two-way power flow which cannot be captured well by traditional model-based techniques. This brings an unprecedented challenge in terms of the accurate localization of faults and proper actions of the protection system. In this paper, we propose a data-driven fault localization strategy based on multi-level system regionalization and the quantification of fault detection results in all subsystems/subregions. This strategy relies on the tree segmentation criterion to divide the entire system under study into several subregions, and then combines Support Vector Data Description (SVDD) and Kernel Density Estimation (KDE) to find the confidence level of fault detection in each subregion in terms of their corresponding p-values. By comparing the p-values, one can accurately localize the faults. Experiments demonstrate that the proposed data-driven fault localization can greatly improve the accuracy of fault localization for distribution systems with high DER penetration.
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Gao, Nannan, Fen Li, Hui Zeng, Daniël van Bilsen, and Martin De Jong. "Can More Accurate Night-Time Remote Sensing Data Simulate a More Detailed Population Distribution?" Sustainability 11, no. 16 (August 19, 2019): 4488. http://dx.doi.org/10.3390/su11164488.
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Aging, shrinking cities, urban agglomerations and other new key terms continue to emerge when describing the large-scale population changes in various cities in mainland China. It is important to simulate the distribution of residential populations at a coarse scale to manage cities as a whole, and at a fine scale for policy making in infrastructure development. This paper analyzes the relationship between the DN (Digital number, value assigned to a pixel in a digital image) value of NPP-VIIRS (the Suomi National Polar-orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite) and LuoJia1-01 and the residential populations of urban areas at a district, sub-district, community and court level, to compare the influence of resolution of remote sensing data by taking urban land use to map out auxiliary data in which first-class (R1), second-class (R2) and third-class residential areas (R3) are distinguished by house price. The results show that LuoJia1-01 more accurately analyzes population distributions at a court level for second- and third-class residential areas, which account for over 85% of the total population. The accuracy of the LuoJia1-01 simulation data is higher than that of Landscan and GHS (European Commission Global Human Settlement) population. This can be used as an important tool for refining the simulation of residential population distributions. In the future, higher-resolution night-time light data could be used for research on accurate simulation analysis that scales down large-scale populations.
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Yoon, Seong-Sim, Anh Tran Phuong, and Deg-Hyo Bae. "Quantitative Comparison of the Spatial Distribution of Radar and Gauge Rainfall Data." Journal of Hydrometeorology 13, no. 6 (December 1, 2012): 1939–53. http://dx.doi.org/10.1175/jhm-d-11-066.1.
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Abstract The common statement that a rain gauge network usually provides better observation at specific points while weather radar provides more accurate observation of the spatial distribution of rain field over a large area has never been subjected to quantitative evaluation. The aim of this paper is to evaluate the statement by using some statistical criteria. The Monte Carlo simulation experiment, inverse distance weighting (IDW) interpolation method, and cross-validation technique are used to investigate the relation between the accuracy of the interpolated rainfall and the rain gauge density. The radar reflectivity–rainfall intensity (Z–R) relationship is constructed by the least squares fitting method from observation data of radar and rain gauges. The variation in this relationship and the accuracy of the radar rainfall with rain gauge density are evaluated by using the Monte Carlo simulation experiment. Three storm events are selected as the case studies. The obtained results show that the accuracy of interpolated and radar rainfall increases nonlinearly with increasing gauge density. The higher correlation coefficient (γ) value of radar-rainfall estimation, compared to gauge interpolation, especially in the convective storm, proves that radar observation provides a more accurate spatial structure of the rain field than gauge observation does.
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Piltz, Ross O. "Accurate data processing for neutron Laue diffractometers." Journal of Applied Crystallography 51, no. 3 (May 25, 2018): 635–45. http://dx.doi.org/10.1107/s1600576718005058.
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The factors affecting the accuracy of structural refinements from image-plate neutron Laue diffractometers are analysed. From this analysis, an improved data-processing method is developed which optimizes the intensity corrections for exposure scaling, wavelength distribution, absorption and extinction corrections, and the wavelength/spatial/time dependence of the image-plate detector efficiencies. Of equal importance is an analysis of the sources of uncertainty in the final corrected intensities, without which bias of the merged intensities occurs, due to the dominance of measurements with small statistical errors though potentially large systematic errors. A new aspect of the impact of detector crosstalk on the counting statistics of area detectors is reported and shown to be significant for the case of neutron Laue diffraction. These methods have been implemented in software which processes data from the KOALA instrument at ANSTO and the now decommissioned VIVALDI instrument at ILL (Grenoble, France). A comparison with earlier data-analysis methods shows a significant improvement in accuracy of the refined structures.
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Karimi, P., and W. G. M. Bastiaanssen. "Spatial evapotranspiration, rainfall and land use data in water accounting – Part 1: Review of the accuracy of the remote sensing data." Hydrology and Earth System Sciences Discussions 11, no. 1 (January 22, 2014): 1073–123. http://dx.doi.org/10.5194/hessd-11-1073-2014.
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Abstract. The scarcity of water encourages scientists to develop new analytical tools to enhance water resource management. Water accounting and distributed hydrological models are examples of such tools. Water accounting needs accurate input data for adequate descriptions of water distribution and water depletion in river basins. Ground-based observatories are decreasing, and remote sensing data is a suitable alternative to measure the required input variables. This paper reviews the reliability of remote sensing algorithms to accurately determine the spatial distribution of actual evapotranspiration, rainfall and land use. For our validation we used only those papers that covered study periods of one season to annual cycles because the accumulated water balance is the primary concern. Review papers covering shorter periods only (days, weeks) were not included in our review. Our review shows that by using remote sensing, the spatial distribution of evapotranspiration can be mapped with an overall accuracy of 95% (STD 5%) and rainfall with an overall accuracy of 82% (STD 15%). Land use can be identified with an overall accuracy of 85% (STD 7%). Hence, more scientific work is needed to improve spatial mapping of rainfall using multiple space-borne sensors. Actual evapotranspiration maps can be used with confidence in water accounting and hydrological modeling.
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Berry, Mari, Brian Peacock, Bobbie Foote, and Lawrence Leemis. "Visual Assessment vs. Statistical Goodness of Fit Tests for Identifying Parent Population." Proceedings of the Human Factors Society Annual Meeting 32, no. 7 (October 1988): 460–64. http://dx.doi.org/10.1177/154193128803200701.
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Statistical tests are used to identify the parent distribution corresponding to a data set. A human observer looking at a histogram can also identify a probability distribution that models the parent distribution. The accuracy of a human observer was compared to the chi-square test for discrete data and the Kolmogorov-Smirnov and chi-square tests for continuous data. The human observer proved more accurate in identifying continuous distributions and the chi-square test proved to be superior in identifying discrete distributions. The effect of sample size and number of intervals in the histogram was included in the experimental design.
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Wan, Haoming, Yunwei Tang, Linhai Jing, Hui Li, Fang Qiu, and Wenjin Wu. "Tree Species Classification of Forest Stands Using Multisource Remote Sensing Data." Remote Sensing 13, no. 1 (January 4, 2021): 144. http://dx.doi.org/10.3390/rs13010144.
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The spatial distribution of forest stands is one of the fundamental properties of forests. Timely and accurately obtained stand distribution can help people better understand, manage, and utilize forests. The development of remote sensing technology has made it possible to map the distribution of tree species in a timely and accurate manner. At present, a large amount of remote sensing data have been accumulated, including high-spatial-resolution images, time-series images, light detection and ranging (LiDAR) data, etc. However, these data have not been fully utilized. To accurately identify the tree species of forest stands, various and complementary data need to be synthesized for classification. A curve matching based method called the fusion of spectral image and point data (FSP) algorithm was developed to fuse high-spatial-resolution images, time-series images, and LiDAR data for forest stand classification. In this method, the multispectral Sentinel-2 image and high-spatial-resolution aerial images were first fused. Then, the fused images were segmented to derive forest stands, which are the basic unit for classification. To extract features from forest stands, the gray histogram of each band was extracted from the aerial images. The average reflectance in each stand was calculated and stacked for the time-series images. The profile curve of forest structure was generated from the LiDAR data. Finally, the features of forest stands were compared with training samples using curve matching methods to derive the tree species. The developed method was tested in a forest farm to classify 11 tree species. The average accuracy of the FSP method for ten performances was between 0.900 and 0.913, and the maximum accuracy was 0.945. The experiments demonstrate that the FSP method is more accurate and stable than traditional machine learning classification methods.
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Kumar, Abhishek, Minho Sung, Jun (Jim) Xu, and Jia Wang. "Data streaming algorithms for efficient and accurate estimation of flow size distribution." ACM SIGMETRICS Performance Evaluation Review 32, no. 1 (June 2004): 177–88. http://dx.doi.org/10.1145/1012888.1005709.
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Weld, Christopher, and Lawrence Leemis. "Mixed-type distribution plots." Information Visualization 18, no. 3 (February 23, 2018): 311–17. http://dx.doi.org/10.1177/1473871618756584.
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Plotting is among the most effective ways to quickly and accurately describe a probability distribution. It makes often complex information accessible, enabling intuition for respective outcomes at a glance. Matters complicate, however, for mixed-type distributions. Mixed-type distributions contain both continuous and discrete components, and accurately portraying those on a single axis can prove difficult—misleading intuition as a consequence of pulling two otherwise disjoint components into focus together. This article examines the challenges of maintaining the simple, concise, and accurate format of traditional probability distribution plots for mixed-type distributions. We illustrate issues arising within this plot classification paradigm, and why a secondary axis is uniquely suited to improve its communication. An algorithm is devised to consistently scale such plots so that they better coincide with intuition. National Football League football starting field position, meteorological data, and financial instruments provide examples demonstrating effectiveness of this plot technique.
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Chan, Jennifer S. K., S. T. Boris Choy, and Udi E. Makov. "Robust Bayesian Analysis of Loss Reserves Data Using the Generalized-t Distribution." ASTIN Bulletin 38, no. 01 (May 2008): 207–30. http://dx.doi.org/10.2143/ast.38.1.2030411.
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This paper presents a Bayesian approach using Markov chain Monte Carlo methods and the generalized-t (GT) distribution to predict loss reserves for the insurance companies. Existing models and methods cannot cope with irregular and extreme claims and hence do not offer an accurate prediction of loss reserves. To develop a more robust model for irregular claims, this paper extends the conventional normal error distribution to the GT distribution which nests several heavy-tailed distributions including the Student-t and exponential power distributions. It is shown that the GT distribution can be expressed as a scale mixture of uniforms (SMU) distribution which facilitates model implementation and detection of outliers by using mixing parameters. Different models for the mean function, including the log-ANOVA, log-ANCOVA, state space and threshold models, are adopted to analyze real loss reserves data. Finally, the best model is selected according to the deviance information criterion (DIC).
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Chan, Jennifer S. K., S. T. Boris Choy, and Udi E. Makov. "Robust Bayesian Analysis of Loss Reserves Data Using the Generalized-t Distribution." ASTIN Bulletin 38, no. 1 (May 2008): 207–30. http://dx.doi.org/10.1017/s0515036100015142.
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This paper presents a Bayesian approach using Markov chain Monte Carlo methods and the generalized-t (GT) distribution to predict loss reserves for the insurance companies. Existing models and methods cannot cope with irregular and extreme claims and hence do not offer an accurate prediction of loss reserves. To develop a more robust model for irregular claims, this paper extends the conventional normal error distribution to the GT distribution which nests several heavy-tailed distributions including the Student-t and exponential power distributions. It is shown that the GT distribution can be expressed as a scale mixture of uniforms (SMU) distribution which facilitates model implementation and detection of outliers by using mixing parameters. Different models for the mean function, including the log-ANOVA, log-ANCOVA, state space and threshold models, are adopted to analyze real loss reserves data. Finally, the best model is selected according to the deviance information criterion (DIC).
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Wang, Ao, Zongkai Zhang, Xiaoming Lei, Ye Xia, and Limin Sun. "All-Weather Thermal Simulation Methods for Concrete Maglev Bridge Based on Structural and Meteorological Monitoring Data." Sensors 21, no. 17 (August 28, 2021): 5789. http://dx.doi.org/10.3390/s21175789.
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Thermal energy exchange induces non-uniform temperature distribution on the concrete bridge structures, leading to variation of static and dynamic properties of structural systems. The finite element method can facilitate thermal simulation and predict the structural temperature distribution based on heat flow theories. Previous studies mainly focused on the daytime with sunny weather, and the effects of solar shadow distribution were not fully considered or even ignored. In this paper, a systematic all-weather thermal simulation method was proposed to investigate the temperature distributions of concrete maglev bridges. The solar shadow distribution on the bridge surface could be accurately simulated to determine the solar radiation-imposed range. A meteorological station and some thermocouples were installed on a real concrete maglev bridge to obtain the real-time structural temperatures and environmental conditions. Its temperature distribution is also simulated using the proposed method within the 27 monitoring days in Summer. Results show that the simulated structural temperature matches well with the measured results under various weather conditions, except that of the east structural surface. Moreover, the simulation method acquired a higher accuracy under overcast or rainy weather due to weaker solar radiation effects. Both the numerical results and experimental records illustrated that direct solar radiation dominates the thermal energy exchange under sunny or cloudy conditions. The proposed methodology for temperature field simulation is oriented by all-weather prediction of structural temperature, which is reliable for concrete bridge structures with the help of accurate measurement of real-time solar radiation.
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Jeffrey, Benjamin, David M. Aanensen, Nicholas J. Croucher, and Samir Bhatt. "Predicting the future distribution of antibiotic resistance using time series forecasting and geospatial modelling." Wellcome Open Research 5 (August 19, 2020): 194. http://dx.doi.org/10.12688/wellcomeopenres.16153.1.
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Background: Increasing antibiotic resistance in a location may be mitigated by changes in treatment policy, or interventions to limit transmission of resistant bacteria. Therefore, accurate forecasting of the distribution of antibiotic resistance could be advantageous. Two previously published studies addressed this, but neither study compared alternative forecasting algorithms or considered spatial patterns of resistance spread. Methods: We analysed data describing the annual prevalence of antibiotic resistance per country in Europe from 2012 – 2016, and the quarterly prevalence of antibiotic resistance per clinical commissioning group in England from 2015 – 2018. We combined these with data on rates of possible covariates of resistance. These data were used to compare the previously published forecasting models, with other commonly used forecasting models, including one geospatial model. Covariates were incorporated into the geospatial model to assess their relationship with antibiotic resistance. Results: For the European data, which was recorded on a coarse spatiotemporal scale, a naïve forecasting model was consistently the most accurate of any of the forecasting models tested. The geospatial model did not improve on this accuracy. However, it did provide some evidence that antibiotic consumption can partially explain the distribution of resistance. The English data were aggregated at a finer scale, and expected-trend-seasonal (ETS) forecasts were the most accurate. The geospatial model did not significantly improve upon the median accuracy of the ETS model, but it appeared to be less sensitive to noise in the data, and provided evidence that rates of antibiotic prescription and bacteraemia are correlated with resistance. Conclusion: Annual, national-level surveillance data appears to be insufficient for fitting accurate antibiotic resistance forecasting models, but there is evidence that data collected at a finer spatiotemporal scale could be used to improve forecast accuracy. Additionally, incorporating antibiotic prescription or consumption data into the model could improve the predictive accuracy.
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Alzeley, Omar, Ehab M. Almetwally, Ahmed M. Gemeay, Huda M. Alshanbari, E. H. Hafez, and M. H. Abu-Moussa. "Statistical Inference under Censored Data for the New Exponential-X Fréchet Distribution: Simulation and Application to Leukemia Data." Computational Intelligence and Neuroscience 2021 (August 29, 2021): 1–16. http://dx.doi.org/10.1155/2021/2167670.
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In reliability studies, the best fitting of lifetime models leads to accurate estimates and predictions, especially when these models have nonmonotone hazard functions. For this purpose, the new Exponential-X Fréchet (NEXF) distribution that belongs to the new exponential-X (NEX) family of distributions is proposed to be a superior fitting model for some reliability models with nonmonotone hazard functions and beat the competitive distribution such as the exponential distribution and Frechet distribution with two and three parameters. So, we concentrated our effort to introduce a new novel model. Throughout this research, we have studied the properties of its statistical measures of the NEXF distribution. The process of parameter estimation has been studied under a complete sample and Type-I censoring scheme. The numerical simulation is detailed to asses the proposed techniques of estimation. Finally, a Type-I censoring real-life application on leukaemia patient’s survival with a new treatment has been studied to illustrate the estimation methods, which are well fitted by the NEXF distribution among all its competitors. We used for the fitting test the novel modified Kolmogorov–Smirnov (KS) algorithm for fitting Type-I censored data.
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Stojmirović, Aleksandar, and Yi-Kuo Yu. "Robust and accurate data enrichment statistics via distribution function of sum of weights." Bioinformatics 26, no. 21 (September 8, 2010): 2752–59. http://dx.doi.org/10.1093/bioinformatics/btq511.
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Schumm, Walter R. "How Accurately Could Early (622-900 C.E.) Muslims Determine the Direction of Prayers (Qibla)?" Religions 11, no. 3 (February 25, 2020): 102. http://dx.doi.org/10.3390/rel11030102.
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Debate has arisen over the ability of Muslim architects in the first two centuries of Islam to determine true qiblas accurately. Some believe that they had such a capability, while others think not. The argument could be more complex—perhaps some architects could, while others could not; perhaps their accuracy changed over time or over greater distances from qibla targets. Here, we investigated how the accurate qiblas of 60 mosques or related structures were, using data from Daniel Gibson’s books and websites. Contrasts were drawn between theories that the qiblas of early mosques were—or were not—generally accurate. If one were to assume that Mecca was the only qibla, qiblas would not appear to have been accurate. However, if one were to assume that qiblas changed, it would be found that qiblas were accurate to plus or minus two degrees in over half of the cases and accurate within plus or minus five degrees in over 80% of cases. Accuracy was not related to distance but did appear to improve over historical time, while distance from the target cities and historical time were positively associated. The average qibla accuracy had a near zero error, with random variations on either side of that zero error. The overall distribution was not normal—kurtotic—because a greater accuracy was found than would have been expected with a normal distribution; however, the pattern deviated more from a uniform distribution than it did from a normal distribution. To try to synthesize the competing theories, we analyzed data for only 14 of the 60 mosques, those presumed to face towards Mecca, and we found fairly high degrees of qibla accuracy with nearly 43% of qiblas within two degrees of accuracy and nearly 80% within five degrees of accuracy. Comparing the accuracy of Meccan qiblas with other qiblas of the same century, we found no significant differences in azimuth errors. While some architects were more accurate than others, early Muslim architects seemed, in general, quite capable of placing qiblas with reasonable accuracy, even though their accuracy may have improved slightly over the first two centuries of Islam.
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Karimi, P., and W. G. M. Bastiaanssen. "Spatial evapotranspiration, rainfall and land use data in water accounting – Part 1: Review of the accuracy of the remote sensing data." Hydrology and Earth System Sciences 19, no. 1 (January 28, 2015): 507–32. http://dx.doi.org/10.5194/hess-19-507-2015.
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Abstract. The scarcity of water encourages scientists to develop new analytical tools to enhance water resource management. Water accounting and distributed hydrological models are examples of such tools. Water accounting needs accurate input data for adequate descriptions of water distribution and water depletion in river basins. Ground-based observatories are decreasing, and not generally accessible. Remote sensing data is a suitable alternative to measure the required input variables. This paper reviews the reliability of remote sensing algorithms to accurately determine the spatial distribution of actual evapotranspiration, rainfall and land use. For our validation we used only those papers that covered study periods of seasonal to annual cycles because the accumulated water balance is the primary concern. Review papers covering shorter periods only (days, weeks) were not included in our review. Our review shows that by using remote sensing, the absolute values of evapotranspiration can be estimated with an overall accuracy of 95% (SD 5%) and rainfall with an overall absolute accuracy of 82% (SD 15%). Land use can be identified with an overall accuracy of 85% (SD 7%). Hence, more scientific work is needed to improve the spatial mapping of rainfall and land use using multiple space-borne sensors. While not always perfect at all spatial and temporal scales, seasonally accumulated actual evapotranspiration maps can be used with confidence in water accounting and hydrological modeling.
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Hennessy, Andrew, Kenneth Clarke, and Megan Lewis. "Generative Adversarial Network Synthesis of Hyperspectral Vegetation Data." Remote Sensing 13, no. 12 (June 8, 2021): 2243. http://dx.doi.org/10.3390/rs13122243.
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New, accurate and generalizable methods are required to transform the ever-increasing amount of raw hyperspectral data into actionable knowledge for applications such as environmental monitoring and precision agriculture. Here, we apply advances in generative deep learning models to produce realistic synthetic hyperspectral vegetation data, whilst maintaining class relationships. Specifically, a Generative Adversarial Network (GAN) is trained using the Cramér distance on two vegetation hyperspectral datasets, demonstrating the ability to approximate the distribution of the training samples. Evaluation of the synthetic spectra shows that they respect many of the statistical properties of the real spectra, conforming well to the sampled distributions of all real classes. Creation of an augmented dataset consisting of synthetic and original samples was used to train multiple classifiers, with increases in classification accuracy seen under almost all circumstances. Both datasets showed improvements in classification accuracy ranging from a modest 0.16% for the Indian Pines set and a substantial increase of 7.0% for the New Zealand vegetation. Selection of synthetic samples from sparse or outlying regions of the feature space of real spectral classes demonstrated increased discriminatory power over those from more central portions of the distributions.
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Menzel, Peter. "Constrained indicator data resampling — A parameter constrained irregular resampling method for scattered point data." GEOPHYSICS 81, no. 2 (March 1, 2016): F17—F26. http://dx.doi.org/10.1190/geo2015-0220.1.
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Resampling of high-resolution data sets is often required for real-time applications in geosciences, e.g., interactive modeling and 3D visualization. To support interactivity and real-time computations, it is often necessary to resample the data sets to a resolution adequate to the application. Conventional resampling approaches create uniformly distributed results, which are not always the best possible solution for particular applications. I have developed a new resampling method called constrained indicator data resampling (CIDRe). This method results in irregular point distributions that are adapted to local parameter signal wavelengths of the given data. The algorithm identifies wavelength variations by analyzing gradients in the given parameter distribution. A higher point density is ensured in areas with larger gradients than in areas with smaller gradients, and thus the resulting data set shows an irregular point distribution. A synthetic data test showed that CIDRe is able to represent a data set better than conventional resampling algorithms. In a second application, CIDRe was used to reduce the number of gravity stations for interactive 3D density modeling, in which the resulting point distribution still allows accurate interactive modeling with a minimum number of data points.
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Feng, Jie, Yong Li, Fengli Xu, and Depeng Jin. "A Bimodal Model to Estimate Dynamic Metropolitan Population by Mobile Phone Data." Sensors 18, no. 10 (October 12, 2018): 3431. http://dx.doi.org/10.3390/s18103431.
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Accurate, real-time and fine-spatial population distribution is crucial for urban planning, government management, and advertisement promotion. Limited by technics and tools, we rely on the census to obtain this information in the past, which is coarse and costly. The popularity of mobile phones gives us a new opportunity to investigate population estimation. However, real-time and accurate population estimation is still a challenging problem because of the coarse localization and complicated user behaviors. With the help of the passively collected human mobility and locations from the mobile networks including call detail records and mobility management signals, we develop a bimodal model beyond the prior work to better estimate real-time population distribution at metropolitan scales. We discuss how the estimation interval, space granularity, and data type will influence the estimation accuracy, and find the data collected from the mobility management signals with the 30 min estimation interval performs better which reduces the population estimation error by 30% in terms of Root Mean Square Error (RMSE). These results show us the great potential of using bimodal model and mobile phone data to estimate real-time population distribution.
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Jung, Chol-Hee, Daniel Park, Peter Georgeson, Khalid Mahmood, Roger Milne, Melissa Southey, and Bernard Pope. "sEst: Accurate Sex-Estimation and Abnormality Detection in Methylation Microarray Data." International Journal of Molecular Sciences 19, no. 10 (October 15, 2018): 3172. http://dx.doi.org/10.3390/ijms19103172.
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DNA methylation influences predisposition, development and prognosis for many diseases, including cancer. However, it is not uncommon to encounter samples with incorrect sex labelling or atypical sex chromosome arrangement. Sex is one of the strongest influencers of the genomic distribution of DNA methylation and, therefore, correct assignment of sex and filtering of abnormal samples are essential for the quality control of study data. Differences in sex chromosome copy numbers between sexes and X-chromosome inactivation in females result in distinctive sex-specific patterns in the distribution of DNA methylation levels. In this study, we present a software tool, sEst, which incorporates clustering analysis to infer sex and to detect sex-chromosome abnormalities from DNA methylation microarray data. Testing with two publicly available datasets demonstrated that sEst not only correctly inferred the sex of the test samples, but also identified mislabelled samples and samples with potential sex-chromosome abnormalities, such as Klinefelter syndrome and Turner syndrome, the latter being a feature not offered by existing methods. Considering that sex and the sex-chromosome abnormalities can have large effects on many phenotypes, including diseases, our method can make a significant contribution to DNA methylation studies that are based on microarray platforms.
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Zhirnov, A. A., and O. B. Kudrjashova. "Peculiarities of data processing for optical measurements of disperse parameters of bimodal media." Computer Optics 43, no. 4 (August 2019): 692–98. http://dx.doi.org/10.18287/2412-6179-2019-43-4-692-698.
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This study is focused on enhancing the informativity of optical measurement techniques for particulate matter. The problem is that the description of particulate matter with bimodal and multimodal distributions by an a priori defined analytical function of particle size distribution (for example, a log-normal distribution) is not accurate enough. Here, we explore if experimental data can be approximated by a multivariable function of particle size distribution instead of using the a priori defined log-normal distribution. For the comparison of the approximation results, experiments are conducted on standard samples with granulometric compositions OGS-01LM and OGS-08LM separately and jointly in a mix. The experimental data are recorded by a high-selectivity turbidimetric technique in water suspensions of these samples. The purpose of this study is to present the measurement results as a distribution function that enables one to identify more accurately the particle-size distribution profile and the corresponding disperse characteristics of the aerosol in question when measuring parameters of disperse media by optical techniques. The main objective of this work is to develop, implement and verify a search algorithm for the particle-size distribution function by way of a multi-parameter function. We show that the solution to the problem proposed herein is more universal because it allows slow and fast processes in suspensions and aerosols to be examined with a lower error. The algorithm can be applied to the problems which are based on solving first-kind Fredholm equations.
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Zhao, Licheng, Yun Shi, Bin Liu, Ciara Hovis, Yulin Duan, and Zhongchao Shi. "Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data." Remote Sensing 11, no. 24 (December 14, 2019): 3012. http://dx.doi.org/10.3390/rs11243012.
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Accurate crop distribution maps provide important information for crop censuses, yield monitoring and agricultural insurance assessments. Most existing studies apply low spatial resolution satellite images for crop distribution mapping, even in areas with a fragmented landscape. Unmanned aerial vehicle (UAV) imagery provides an alternative imagery source for crop mapping, yet its spectral resolution is usually lower than satellite images. In order to produce more accurate maps without losing any spatial heterogeneity (e.g., the physical boundary of land parcel), this study fuses Sentinel-2A and UAV images to map crop distribution at a finer spatial scale (i.e., land parcel scale) in an experimental site with various cropping patterns in Heilongjiang Province, Northeast China. Using a random forest algorithm, the original, as well as the fused images, are classified into 10 categories: rice, corn, soybean, buckwheat, other vegetations, greenhouses, bare land, water, roads and houses. In addition, we test the effect of UAV image choice by fusing Sentinel-2A with different UAV images at multiples spatial resolutions: 0.03 m, 0.10 m, 0.50 m, 1.00 m and 3.00 m. Overall, the fused images achieved higher classification accuracies, ranging between 10.58% and 16.39%, than the original images. However, the fused image based on the finest UAV image (i.e., 0.03 m) does not result in the highest accuracy. Instead, the 0.10 m spatial resolution UAV image produced the most accurate map. When the spatial resolution is less than 0.10 m, accuracy decreases gradually as spatial resolution decreases. The results of this paper not only indicate the possibility of combining satellite images and UAV images for land parcel level crop mapping for fragmented landscapes, but it also implies a potential scheme to exploit optimal choice of spatial resolution in fusing UAV images and Sentinel-2A, with little to no adverse side-effects.
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Smith, Bruce R., Christophe M. Herbinger, and Heather R. Merry. "Accurate Partition of Individuals Into Full-Sib Families From Genetic Data Without Parental Information." Genetics 158, no. 3 (July 1, 2001): 1329–38. http://dx.doi.org/10.1093/genetics/158.3.1329.
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Abstract Two Markov chain Monte Carlo algorithms are proposed that allow the partitioning of individuals into full-sib groups using single-locus genetic marker data when no parental information is available. These algorithms present a method of moving through the sibship configuration space and locating the configuration that maximizes an overall score on the basis of pairwise likelihood ratios of being full-sib or unrelated or maximizes the full joint likelihood of the proposed family structure. Using these methods, up to 757 out of 759 Atlantic salmon were correctly classified into 12 full-sib families of unequal size using four microsatellite markers. Large-scale simulations were performed to assess the sensitivity of the procedures to the number of loci and number of alleles per locus, the allelic distribution type, the distribution of families, and the independent knowledge of population allelic frequencies. The number of loci and the number of alleles per locus had the most impact on accuracy. Very good accuracy can be obtained with as few as four loci when they have at least eight alleles. Accuracy decreases when using allelic frequencies estimated in small target samples with skewed family distributions with the pairwise likelihood approach. We present an iterative approach that partly corrects that problem. The full likelihood approach is less sensitive to the precision of allelic frequencies estimates but did not perform as well with the large data set or when little information was available (e.g., four loci with four alleles).
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Ito, T., T. Kato, K. Takagishi, S. Okabe, and D. Sano. "Bayesian modeling of virus removal efficiency in wastewater treatment processes." Water Science and Technology 72, no. 10 (August 1, 2015): 1789–95. http://dx.doi.org/10.2166/wst.2015.402.
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Left-censored datasets of virus density in wastewater samples make it difficult to evaluate the virus removal efficiency in wastewater treatment processes. In the present study, we modeled the probabilistic distribution of virus removal efficiency in a wastewater treatment process with a Bayesian approach, and investigated how many detect samples in influent and effluent are necessary for accurate estimation. One hundred left-censored data of virus density in wastewater (influent and effluent) were artificially generated based on assumed log-normal distributions and the posterior predictive distribution of virus density, and the log-ratio distribution were estimated. The estimation accuracy of distributions was quantified by Bhattacharyya coefficient. When it is assumed that the accurate estimation of posterior predictive distributions is possible when a 100% positive rate is obtained for 12 pairs of influent and effluent, 11 out of 144, 60 out of 324, and 201 out of 576 combinations of detect samples gave an accurate estimation at the significant level of 0.01 in a Kruskal-Wallis test when the total sample number was 12, 18, and 24, respectively. The combinations with the minimum number of detect samples were (12, 9), (16, 10), and (21, 8) when the total sample number was 12, 18, and 24, respectively.
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Tomz, Michael, Joshua A. Tucker, and Jason Wittenberg. "An Easy and Accurate Regression Model for Multiparty Electoral Data." Political Analysis 10, no. 1 (2002): 66–83. http://dx.doi.org/10.1093/pan/10.1.66.
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Katz and King have previously proposed a statistical model for multiparty election data. They argue that ordinary least-squares (OLS) regression is inappropriate when the dependent variable measures the share of the vote going to each party, and they recommend a superior technique. Regrettably, the Katz-King model requires a high level of statistical expertise and is computationally demanding for more than three political parties. We offer a sophisticated yet convenient alternative that involves seemingly unrelated regression (SUR). SUR is nearly as easy to use as OLS yet performs as well as the Katz-King model in predicting the distribution of votes and the composition of parliament. Moreover, it scales easily to an arbitrarily large number of parties. The model has been incorporated intoClarify, a statistical suite that is available free on the Internet.
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Löhner, Rainald, and Harbir Antil. "Determination of volumetric material data from boundary measurements." International Journal of Numerical Methods for Heat & Fluid Flow 30, no. 11 (February 20, 2020): 4837–63. http://dx.doi.org/10.1108/hff-12-2019-0931.
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Purpose The purpose of this study is to determine the possibility of an accurate assessment of the spatial distribution of material properties such as conductivities or impedances from boundary measurements when the governing partial differential equation is a Laplacian. Design/methodology/approach A series of numerical experiments were carefully performed. The results were analyzed and compared. Findings The results to date show that while the optimization procedure is able to obtain spatial distributions of the conductivity k that reduce the cost function significantly, the resulting conductivity k is still significantly different from the target (or real) distribution sought. While the normal fluxes recovered are very close to the prescribed ones, the tangential fluxes can differ considerably. Research limitations/implications At this point, it is not clear why rigorous mathematical proofs yield results of convergence and uniqueness, while in practice, accurate distributions of the conductivity k seem to be elusive. One possible explanation is that the spatial influence of conductivities decreases exponentially with distance. Thus, many different conductivities inside a domain could give rise to very similar (infinitely close) boundary measurements. Practical implications This implies that the estimation of field conductivities (or generally field data) from boundary data is far more difficult than previously assumed when the governing partial differential equation in the domain is a Laplacian. This has consequences for material parameter assessments (e.g. for routine maintenance checks of structures), electrical impedance tomography, and many other applications. Originality/value This is the first time such a finding has been reported in this context.
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Yoneda, Kiyoshi. "Elevator Trip Distribution for Inconsistent Passenger Input-Output Data." Decision Making in Manufacturing and Services 1, no. 2 (October 11, 2007): 175–90. http://dx.doi.org/10.7494/dmms.2007.1.2.175.
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Accurate traffic data are the basis for group control of elevators and its performance evaluation by trace driven simulation. The present practice estimates a time series of inter-floor passenger traffic based on commonly available elevator sensor data. The method demands that the sensor data be transformed into sets of passenger input-output data which are consistent in the sense that the transportation preserves the number of passengers. Since observation involves various behavioral assumptions, which may actually be violated, as well as measurement errors, it has been necessary to apply data adjustment procedures to secure the consistency. This paper proposes an alternative algorithm which reconstructs elevator passenger origin-destination tables from inconsistent passenger input-output data sets, thus eliminating the ad hoc data adjustment.
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Gayrard, Emeline, Cédric Chauvière, Hacène Djellout, and Pierre Bonnet. "MODELING EXPERIMENTAL DATA WITH POLYNOMIALS CHAOS." Probability in the Engineering and Informational Sciences 34, no. 1 (August 14, 2018): 14–26. http://dx.doi.org/10.1017/s026996481800030x.
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Given a raw data sample, the purpose of this paper is to design a numerical procedure to model this sample under the form of polynomial chaos expansion. The coefficients of the polynomial are computed as the solution to a constrained optimization problem. The procedure is first validated on samples coming from a known distribution and it is then applied to raw experimental data of unknown distribution. Numerical experiments show that only five coefficients of the Chaos expansions are required to get an accurate representation of a sample.
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Juba, Brendan, and Hengxuan Li. "More Accurate Learning of k-DNF Reference Classes." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4385–93. http://dx.doi.org/10.1609/aaai.v34i04.5864.
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In machine learning, predictors trained on a given data distribution are usually guaranteed to perform well for further examples from the same distribution on average. This often may involve disregarding or diminishing the predictive power on atypical examples; or, in more extreme cases, a data distribution may be composed of a mixture of individually “atypical” heterogeneous populations, and the kind of simple predictors we can train may find it difficult to fit all of these populations simultaneously. In such cases, we may wish to make predictions for an atypical point by selecting a suitable reference class for that point: a subset of the data that is “more similar” to the given query point in an appropriate sense. Closely related tasks also arise in applications such as diagnosis or explaining the output of classifiers. We present new algorithms for computing k-DNF reference classes and establish much stronger approximation guarantees for their error rates.
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Khan, I., D. Shan, and Q. Li. "Modal Parameter Identification Of Cable Stayed Bridge Based On Exploratory Data Analysis." Archives of Civil Engineering 61, no. 2 (June 1, 2015): 3–22. http://dx.doi.org/10.1515/ace-2015-0011.
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AbstractIn order to identify the modal parameters of civil structures it is vital to distinguish the defective data from that of appropriate and accurate data. The defects in data may be due to various reasons like defects in the data collection, malfunctioning of sensors, etc. For this purpose Exploratory Data Analysis (EDA) was engaged to envisage the distribution of sensor’s data and to detect the malfunctioning with in the sensors. Then outlier analysis was performed to remove those data points which may disrupt the accurate data analysis. Then Data Driven Stochastic Sub-space Identification (DATA-SSI) was engaged to perform the modal parameter identification. In the end to validate the accuracy of the proposed method stabilization diagrams were plotted. Sutong Bridge, one of the largest span cable stayed bridge was used as a case study and the suggested technique was employed. The results obtained after employing the above mentioned techniques are very valuable, accurate and effective.
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Mao, Yeying, Zhengyu Huang, Changsen Feng, Hui Chen, Qiming Yang, and Junchang Ma. "An Early Warning Method of Distribution System Fault Risk Based on Data Mining." Mathematical Problems in Engineering 2020 (December 2, 2020): 1–10. http://dx.doi.org/10.1155/2020/8880661.
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Accurate warning information of potential fault risk in the distribution network is essential to the economic operation as well as the rational allocation of maintenance resources. In this paper, we propose a fault risk warning method for a distribution system based on an improved RelieF-Softmax algorithm. Firstly, four categories including 24 fault features of the distribution system are determined through data investigation and preprocessing. Considering the frequency of distribution system faults, and then their consequences, the risk classification method of the distribution system is presented. Secondly, the K-maxmin clustering algorithm is introduced to improve the random sampling process, and then an improved RelieF feature extraction method is proposed to determine the optimal feature subset with the strongest correlation and minimum redundancy. Finally, the loss function of Softmax is improved to cope with the influence of sample imbalance on the prediction accuracy. The optimal feature subset and Softmax classifier are applied to forewarn the fault risk in the distribution system. The 191-feeder power distribution system in south China is employed to demonstrate the effectiveness of the proposed method.
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Ward, M. J., and A. M. Zambone. "The U.S. Federal Data-Collection Process for Children and Youths who are Deaf-Blind." Journal of Visual Impairment & Blindness 86, no. 10 (December 1992): 429–34. http://dx.doi.org/10.1177/0145482x9208601006.
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Obtaining accurate counts of children who are deaf-blind is important for planning resource distribution and program development and implementation. Yet accuracy has been difficult to achieve for a number of reasons. This article reports the results of policy research that examined various strategies implemented by the U.S. federal government to obtain data on child counts and the issues related to this effort and its outcomes.
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Myles, JP, GM Price, N. Hunter, M. Day, and SW Duffy. "A potentially useful distribution model for dietary intake data." Public Health Nutrition 6, no. 5 (August 2003): 513–19. http://dx.doi.org/10.1079/phn2003459.
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AbstractBackground:Conventional mixed models for the analysis of diet diary data have introduced several simplifying assumptions, such as that of a single standard deviation for within-person day-to-day variation which is common to all individuals.Objective:We developed a model in which the within-person standard deviation was allowed to differ from person to person.Design:The model was demonstrated using data on daily retinol intake from the Dietary and Nutritional Survey of British Adults. The data were from 7-day weighed dietary diaries. Estimation was performed by Markov chain Monte Carlo. Reliability of the model was assessed from the accuracy of estimation of the percentage of days on which various intakes were exceeded. For levels above the median retinol intake, estimation of percentages of days with excessive intakes was most accurate using the model with varying within-person standard deviation.Setting:A survey of British adults aged 16–64 years.Subjects:In total 2197 adults living in the UK, 1087 males and 1110 females.Results:Under the traditional model, estimated daily intake ranged from 716.4 to 1421.8 μg depending on age and sex, with a within-person standard deviation of 4298.9 μg. Under the new model, estimated average daily intake ranged from 388.9 to 518.3 μg depending on age and sex, but with a within-person standard deviation varying between subjects with a 95% range of 29 to 8384 μg. The new model was shown to predict the percentage of days of exceeding large intakes more successfully than the traditional model. For example, the percentage of days of exceeding the maximum recommended intake (9000 μg for men and 7500 μg for women) was 2.4%. The traditional model predicted no excessive intakes, whereas the new model predicted 2.9%.Conclusions:This model is potentially useful in dietary research in general and for analysis of data on chemical contaminants in foods, in particular.
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Angelliaume, Sébastien, Luke Rosenberg, and Matthew Ritchie. "Modeling the Amplitude Distribution of Radar Sea Clutter." Remote Sensing 11, no. 3 (February 6, 2019): 319. http://dx.doi.org/10.3390/rs11030319.
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Ship detection in the maritime domain is best performed with radar due to its ability to surveil wide areas and operate in almost any weather condition or time of day. Many common detection schemes require an accurate model of the amplitude distribution of radar echoes backscattered by the ocean surface. This paper presents a review of select amplitude distributions from the literature and their ability to represent data from several different radar systems operating from 1 GHz to 10 GHz. These include the K distribution, arguably the most popular model from the literature as well as the Pareto, K+Rayleigh, and the trimodal discrete (3MD) distributions. The models are evaluated with radar data collected from a ground-based bistatic radar system and two experimental airborne radars. These data sets cover a wide range of frequencies (L-, S-, and X-band), and different collection geometries and sea conditions. To guide the selection of the most appropriate model, two goodness of fit metrics are used, the Bhattacharyya distance which measures the overall distribution error and the threshold error which quantifies mismatch in the distribution tail. Together, they allow a quantitative evaluation of each distribution to accurately model radar sea clutter for the purpose of radar ship detection.
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Bailey, James M., and Keith M. Gregg. "A Technique for Population Pharmacodynamic Analysis of Concentration-Binary Response Data." Anesthesiology 86, no. 4 (April 1, 1997): 825–35. http://dx.doi.org/10.1097/00000542-199704000-00013.
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Background Pharmacodynamic data frequently consist of the binary assessment (a "yes" or "no" answer) of the response to a defined stimulus (verbal stimulus, intubation, skin incision, and so on) for multiple patients. The concentration-effect relation is usually reported in terms of C50, the drug concentration associated with a 50% probability of drug effect, and a parameter the authors denote gamma, which determines the shape of the concentration-probability of effect curve. Accurate estimation of gamma, a parameter describing the entire curve, is as important as the estimation of C50, a single point on this curve. Pharmacodynamic data usually are analyzed without accounting for interpatient variability. The authors postulated that accounting for interpatient variability would improve the accuracy of estimation of gamma and allow the estimation of C50 variability. Methods A probit-based model for the individual concentration-response relation was assumed, characterized by two parameters, C50 and gamma. This assumption was validated by comparing probit regression with the more commonly used logistic regression of data from individual patients found in the anesthesiology literature. The model was then extended to analysis of population data by assuming that C50 has a log-normal distribution. Population data were analyzed in terms of three parameters, (C50), the mean value of C50 in the population; omega, the standard deviation of the distribution of the logarithm of C50; and gamma. The statistical characteristics of the technique were assessed using simulated data. The data were generated for a range of gamma and omega values, assuming that C50 and gamma had a log-normal distribution. Results The probit-based model describes data from individual patients and logistic regression does. Population analysis using the extended probit model accurately estimated (C50), gamma, and omega for a range of values, despite the fact that the technique accounts for C50 variability but not gamma variability. Conclusions A probit-based method of pharmacodynamic analysis of pooled population data facilitates accurate estimation of the concentration-response curve.
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Chen, Y., Z. Liu, W. Zhang, C. Qiao, and H. Gu. "EXTRACTION OF LEAF ANGLE DISTRIBUTION FROM AN INDIVIDUAL BROADLEAF TREE USING TERRESTRIAL LASER SCANNING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 957–62. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-957-2019.
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<p><strong>Abstract.</strong> The angular distribution of leaves is a key vegetation structural parameter for evaluating the reflection and transmission of solar radiation through vegetation canopies. Accurate extraction of Leaf Angle Distribution (LAD) is of great importance in estimating other vegetation structural parameters such as the canopy clumping and leaf area index. However, field measurement of LAD is time-consuming, labour-intensive and subjective. In most studies, LAD is assumed to follow the spherical distribution assumption within canopy which may lead to considerable errors. To address this issue, we proposed a new approach for leaf segmentation and LAD measurement of individual broadleaf tree based on the TLS point cloud data. Based on the point density, point continuity and the distribution of intensity in the point cloud, this approach provides a fast and accurate leaf segmentation and LAD extraction strategy. Results of this TLS-based LAD method compared well with that extracted by the field measurement and the MDI-based method. This strategy shows its potential and applicability in accurate LAD measurement and LAI estimation.</p>
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Jargowsky, Paul A., and Christopher A. Wheeler. "Estimating Income Statistics from Grouped Data: Mean-constrained Integration over Brackets." Sociological Methodology 48, no. 1 (July 9, 2018): 337–74. http://dx.doi.org/10.1177/0081175018782579.
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Researchers studying income inequality, economic segregation, and other subjects must often rely on grouped data—that is, data in which thousands or millions of observations have been reduced to counts of units by specified income brackets. The distribution of households within the brackets is unknown, and highest incomes are often included in an open-ended top bracket, such as “$200,000 and above.” Common approaches to this estimation problem include calculating midpoint estimators with an assumed Pareto distribution in the top bracket and fitting a flexible multiple-parameter distribution to the data. The authors describe a new method, mean-constrained integration over brackets (MCIB), that is far more accurate than those methods using only the bracket counts and the overall mean of the data. On the basis of an analysis of 297 metropolitan areas, MCIB produces estimates of the standard deviation, Gini coefficient, and Theil index that are correlated at 0.997, 0.998, and 0.991, respectively, with the parameters calculated from the underlying individual record data. Similar levels of accuracy are obtained for percentiles of the distribution and the shares of income by quintiles of the distribution. The technique can easily be extended to other distributional parameters and inequality statistics.
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Yang, Yixin, Xin Lü, Jian Ma, and Han Qiao. "A Robust Factor Analysis Model for Dichotomous Data." Journal of Systems Science and Information 2, no. 5 (October 25, 2014): 437–50. http://dx.doi.org/10.1515/jssi-2014-0437.
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AbstractFactor analysis is widely used in psychology, sociology and economics, as an analytically tractable method of reducing the dimensionality of the data in multivariate statistical analysis. The classical factor analysis model in which the unobserved factor scores and errors are assumed to follow the normal distributions is often criticized because of its lack of robustness. This paper introduces a new robust factor analysis model for dichotomous data by using robust distributions such as multivariatet-distribution. After comparing the fitting results of the normal factor analysis model and the robust factor analysis model for dichotomous data, it can been seen that the robust factor analysis model can get more accurate analysis results in some cases, which indicates this model expands the application range and practical value of the factor analysis model.
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Jiao, Tan, Xue Hui Wen, and Xiang Wang. "Study on Probability Distribution Models of the Subgrade Continuous Compaction Indicator." Applied Mechanics and Materials 438-439 (October 2013): 1060–64. http://dx.doi.org/10.4028/www.scientific.net/amm.438-439.1060.
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The paper based on the site of a subgrade compaction test data, used the continuous compaction indicator CMV as the analysis variable, filtered the date based on the influencing factors of CMV, and used the common probability models for statistical analysis,finally got the probability distribution models with which the CMV data comply, then chose the most accurate distribution model. The result showed that the lognormal distribution was the most accurate distribution. After being examined in two other test sections, the lognormal distribution was also the most accurate distribution. It provides a reference for the continuous compaction quality testing of the subgrade.
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Häussling Löwgren, Bartolomeus, Joris Weigert, Erik Esche, and Jens-Uwe Repke. "Uncertainty Analysis for Data-Driven Chance-Constrained Optimization." Sustainability 12, no. 6 (March 20, 2020): 2450. http://dx.doi.org/10.3390/su12062450.
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In this contribution our developed framework for data-driven chance-constrained optimization is extended with an uncertainty analysis module. The module quantifies uncertainty in output variables of rigorous simulations. It chooses the most accurate parametric continuous probability distribution model, minimizing deviation between model and data. A constraint is added to favour less complex models with a minimal required quality regarding the fit. The bases of the module are over 100 probability distribution models provided in the Scipy package in Python, a rigorous case-study is conducted selecting the four most relevant models for the application at hand. The applicability and precision of the uncertainty analyser module is investigated for an impact factor calculation in life cycle impact assessment to quantify the uncertainty in the results. Furthermore, the extended framework is verified with data from a first principle process model of a chloralkali plant, demonstrating the increased precision of the uncertainty description of the output variables, resulting in 25% increase in accuracy in the chance-constraint calculation.
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Cheng, Weiping, Gang Xu, Hongji Fang, and Dandan Zhao. "Study on Pipe Burst Detection Frame Based on Water Distribution Model and Monitoring System." Water 11, no. 7 (June 30, 2019): 1363. http://dx.doi.org/10.3390/w11071363.
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This paper describes an infrastructure to detect burst events in a water distribution network, which we illustrate using the Guangzhou water distribution system (WDS). We consider three issues: The feasibility and capability of accurate detection, the layout and design of the monitoring infrastructure, and the burst event detection algorithm. Background noise is identified by analyzing the monitored data. A burst event can be accurately detected only when the impact of the burst can be differentiated from the background noise. We hypothesize that there is a minimum pipe diameter below which accurate burst detection is impossible. We found that data from at least two sensors close to the burst event are required to reduce detection errors.
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Yu, Sheng, Yumeng Ma, Jessica Gronsbell, Tianrun Cai, Ashwin N. Ananthakrishnan, Vivian S. Gainer, Susanne E. Churchill, et al. "Enabling phenotypic big data with PheNorm." Journal of the American Medical Informatics Association 25, no. 1 (November 3, 2017): 54–60. http://dx.doi.org/10.1093/jamia/ocx111.
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Abstract Objective Electronic health record (EHR)-based phenotyping infers whether a patient has a disease based on the information in his or her EHR. A human-annotated training set with gold-standard disease status labels is usually required to build an algorithm for phenotyping based on a set of predictive features. The time intensiveness of annotation and feature curation severely limits the ability to achieve high-throughput phenotyping. While previous studies have successfully automated feature curation, annotation remains a major bottleneck. In this paper, we present PheNorm, a phenotyping algorithm that does not require expert-labeled samples for training. Methods The most predictive features, such as the number of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes or mentions of the target phenotype, are normalized to resemble a normal mixture distribution with high area under the receiver operating curve (AUC) for prediction. The transformed features are then denoised and combined into a score for accurate disease classification. Results We validated the accuracy of PheNorm with 4 phenotypes: coronary artery disease, rheumatoid arthritis, Crohn’s disease, and ulcerative colitis. The AUCs of the PheNorm score reached 0.90, 0.94, 0.95, and 0.94 for the 4 phenotypes, respectively, which were comparable to the accuracy of supervised algorithms trained with sample sizes of 100–300, with no statistically significant difference. Conclusion The accuracy of the PheNorm algorithms is on par with algorithms trained with annotated samples. PheNorm fully automates the generation of accurate phenotyping algorithms and demonstrates the capacity for EHR-driven annotations to scale to the next level – phenotypic big data.
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Dmowska, Anna, and Tomasz F. Stepinski. "Racial Dot Maps Based on Dasymetrically Modeled Gridded Population Data." Social Sciences 8, no. 5 (May 18, 2019): 157. http://dx.doi.org/10.3390/socsci8050157.
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Racial geography, mapping spatial distributions of different racial groups, is of keen interest in a multiracial society like the United States. A racial dot map is a method of visualizing racial geography, which depicts spatial distribution, population density, and racial mix in a single, easy-to-understand map. Because of the richness of information it carries, the dot map is an excellent tool for visual analysis of racial distribution. Presently-used racial dot maps are based on the Census data at the tract or the block level. In this paper, we present a method of constructing a more spatially-accurate racial dot map based on a sub-block-resolution population grid. The utility of our dot maps is further enhanced by placing dots on the map in random order regardless of the race they represent in order to achieve a more accurate depiction of local racial composition. We present a series of comparisons between dot maps based on tract, block, and grid data. The advantage of a grid-based dot map is evident from the visual comparison of all maps with an actual image of the mapped area. We make available the R code for constructing grid-based dot maps. We also make available 2010 grid-based racial dot maps for all counties in the conterminous United States.