Scale Selection Research Articles

Inclusion measure-based multi-granulation decision-theoretic rough sets in multi-scale intuitionistic fuzzy information tables

Multi-granulation rough sets (MGRSs) and decision-theoretic rough sets (DTRSs) are important and popular extended types of Pawlak’s classical rough set model. Multi-granulation DTRSs (MG-DTRSs), which combine these two generalized rough set models, have been investigated in depth in recent years to handle noisy distributed data. However, this combination cannot be used to acquire knowledge from multi-scale information systems, in which an object may take on different values under the same attribute depending on the scale used to measure it. Two novel types of MG-DTRSs in multi-scale intuitionistic fuzzy (IF) information tables have been developed on the basis of IF inclusion measures in this study to solve this problem. First, we introduce a type of inclusion measure between two IF sets and present the concept of inclusion measure-based DTRSs in multi-scale IF information tables. Second, we present the inclusion measure-based optimistic and pessimistic MG-DTRSs in multi-scale IF information tables, examine their properties, and analyze the three-way decision method based on the presented models. Third, we define the optimal scale selection and present the two optimal scale selection algorithms based on MG-DTRSs in multi-scale IF information tables. Fourth, we provide the reducts of the optimal scales based on MG-DTRSs in multi-scale IF information tables, examine the discernibility function reduction method, and devise two algorithms for computing an optimal approximation scale reduct. Finally, we discuss several possible generalizations related to MG-DTRSs in multi-scale IF information tables. This study provides an MG-DTRS method for acquiring knowledge from multi-scale IF information tables.

An Approach to Study Groundwater Flow Field Evolution Time Scale Effects and Mechanisms

The temporal scale effect is an important issue for groundwater system evolution research. The selection of an appropriate time scale will enhance the understanding of the characteristics and mechanisms of groundwater flow field evolution. In this study, a methodology was provided to analyze the groundwater system evolution, focusing on the choice of the suitable time step for identifying the distinct stages of evolution, characterized by different behavior linked to the management of the groundwater system. The evolution trend of the groundwater level in the center of the cone of depression at different time scales, combined with the F test and the groundwater system balance index (Re) categories, were used for the choice of the time step and the division of the evolution stages. Based on the transformed groundwater level time series using the selected best time step, the main factors controlling the groundwater evolution were assessed for the different stages. Our results show that the methodology can exactly identify the different important stages of the evolution, and they can be used to individually study these stages, which can help to reveal the mechanisms of the groundwater evolution more easily. Therefore, it is useful to obtain an increased knowledge of the regional groundwater dynamics.

Multi-class segmentation of neuronal structures in electron microscopy images

BackgroundSerial block face scanning electron microscopy (SBFEM) is becoming a popular technology in neuroscience. We have seen in the last years an increasing number of works addressing the problem of segmenting cellular structures in SBFEM images of brain tissue. The vast majority of them is designed to segment one specific structure, typically membranes, synapses and mitochondria. Our hypothesis is that the performance of these algorithms can be improved by concurrently segmenting more than one structure using image descriptions obtained at different scales.ResultsWe consider the simultaneous segmentation of two structures, namely, synapses with mitochondria, and mitochondra with membranes. To this end we select three image stacks encompassing different SBFEM acquisition technologies and image resolutions. We introduce both a new Boosting algorithm to perform feature scale selection and the Jaccard Curve as a tool compare several segmentation results. We then experimentally study the gains in performance obtained when simultaneously segmenting two structures with properly selected image descriptor scales. The results show that by doing so we achieve significant gains in segmentation accuracy when compared to the best results in the literature.ConclusionsSimultaneously segmenting several neuronal structures described at different scales provides voxel classification algorithms with highly discriminating features that significantly improve segmentation accuracy.

Measuring Hyperscale Topographic Anisotropy as a Continuous Landscape Property

Several landforms are known to exhibit topographic anisotropy, defined as a directional inequality in elevation. The quantitative analysis of topographic anisotropy has largely focused on measurements taken from specific landforms, ignoring the surrounding landscape. Recent research has made progress in measuring topographic anisotropy as a distributed field in natural landscapes. However, current methods are computationally inefficient, as they require specialized hardware and computing environments, or have a limited selection of scales that undermines the feasibility and quality of multiscale analyses by introducing bias. By necessity, current methods operate with a limited set of scales, rather than the full distribution of possible landscapes. Therefore, we present a method for measuring topographic anisotropy in the landscape that has the computational efficiency required for hyperscale analysis by using the integral image filtering approach to compute oriented local topographic position (LTP) measurements, coupled with a root-mean-square deviation (RMSD) model that compares directional samples to an omnidirectional sample. Two tools were developed: One to output a scale signature for a single cell, and the other to output a raster containing the maximum anisotropy value across a range of scales. The performances of both algorithms were tested using two data sets containing repetitive, similarly sized and oriented anisotropic landforms, including a dune field and a drumlin field. The results demonstrated that the method presented has the robustness and sensitivity to identify complex hyperscale anisotropy such as nested features (e.g., a drumlin located within a valley).

The importance of core habitat for a threatened species in changing landscapes

Abstract Habitat loss, fragmentation, and alteration of the landscape matrix are interdependent processes, collectively responsible for most recent species extinctions. Thus, determining the extent to which these landscape processes affect animals is critical for conservation. However, researchers have often assumed that interdependent effects are independently related to animals’ responses, underestimating the importance of one or several landscape processes in driving species declines. We demonstrate how to disentangle the interdependent effects of habitat amount, fragmentation, and edge context on population size by assessing abundance of a rapidly declining grassland songbird species (grasshopper sparrow Ammodramus savannarum) in eastern Kansas (USA). We conducted >7,000 point count surveys at >2,000 sites over two breeding seasons, then modelled the direct, interactive, and indirect effects of landscape factors on abundance within spatial scales (200‐, 400‐, 800‐, and 1,600‐m radii) relevant to our focal species’ dispersal behaviour. Sparrow abundance correlated most strongly with landscape structure within 400‐m radii, increasing nonlinearly with grassland area and decreasing with the proportion of grassland near cropland or woody edges. Sparrows’ negative response to cropland edges was mostly an added, indirect consequence of reduced grassland area, whereas sparrows’ stronger negative response to woody edges was not attributable to variation in grassland area. Fragmentation and edge context mattered most in landscapes comprising c. 50%–80% grassland. Synthesis and applications. In our research, abundance of a threatened grassland songbird was influenced more by core grassland area (a function of total grassland area, fragmentation, and edge context) than total grassland area per se. Moreover, a local extinction threshold of c. 50% grassland indicated that small amounts of habitat were unsuitable for our focal species regardless of habitat configuration or matrix type. Local extinction thresholds in response to habitat area provide clear baseline targets for land managers; above those thresholds, configuration and the matrix can be modified to increase abundance of edge‐sensitive animals. Conflicting evidence in the literature regarding the importance of fragmentation and matrix features could be partially explained by species‐level traits, or methodological issues such as defining landscapes at ecologically arbitrary spatial scales, assessing landscape quality using species richness, and ignoring interactive and indirect effects.

Hyperspectral image classification based on adaptive segmentation

Object-based hyperspectral image classification (OBHIC) converts the basic unit from ‘pixel’ to ‘object’ by image segmentation, in order to take advantage of the spatial distribution law of geographical substances, as well as increase classification performances. However, it involves the problem of scale selection, i.e. the segmentation parameters are set manually by empirical values. In this paper, a novel OBHIC algorithm based on adaptive segmentation is proposed. Here, hyperspectral images (HSIs) are first segmented through a new segmentation scheme with constraint ability, and the thresholds for segmentation are calculated adaptively by utilizing training samples. And then K-nearest neighbor algorithm (KNN) is applied to classify the centers of each region after segmentation. In addition, based on the semisupervised idea, semi-known samples are obtained to further improve the classification performance. Experimental results are presented on two HSI datasets. For different HSIs, the adaptive thresholds calculated are consistent with empirical ones, and the developed classification algorithm has achieved good classification results, thus demonstrating strong robustness of the algorithm. For the HSI Indian Pines from AVIRIS sensor, the Overall Accuracy (OA) and kappa are 95.13% and 0.9444 respectively with 10% training samples, and for the HSI Pavia University from ROSIS sensor, the OA and kappa are 95.52% and 0.9416 respectively with 2% training samples. And good classification performance is still maintained for small number of training samples.

Sparse MMC-LES of a Sydney swirl flame

Sparse multiple mapping conditioning-large eddy simulation (MMC-LES) is applied for the first time to a flame involving swirl, thus extending the model to complex flow conditions that are more indicative of practical combustors than is the case for the jet flames simulated previously. Two cases from the University of Sydney swirl flame series are chosen: (i) non-reacting case N16S159 is simulated first to test the resolution of the unstructured mesh and boundary conditions; followed by (ii) methane–air swirl flame SMA2 which has the same flow boundary conditions as N16S159. MMC-LES is a filtered density function (FDF) approach and accurate emulation of the subfilter conditional fluctuations is sensitive to the modelling of the mixing time scale. Two models are tested: (i) the original mixing time scale model (C&K model) proposed by Cleary and Klimenko (2011) and used in all past MMC-LES of laboratory flame cases, and (ii) a new, consistently anisotropic model (a-ISO model) proposed recently by Vo et al. (2017) and validated against direct numerical simulation (DNS) only. Overall, the swirl flow predictions are in very good agreement with the experimental data and both mixing time scale models produce similar statistical results for axial and azimuthal velocity, mixture fraction and unconditional reacting scalars including temperature and CO and OH mass fraction. While the predictions of conditional mean temperature show only a minor sensitivity to the mixing time scale selection, the a-ISO model produces significantly better results for the conditional fluctuations of temperature.

USING TIME SCALES WHILE APPROXIMATING THE LENGTH OF COMPUTER NETWORKS

The subject of the research is to predict the queue length for the communication device of a high-speed computer network with non-Gaussian traffic. The goal of this article is to examine the probabilities of the application of time scales used to study the organization of queues of modern high-speed computer networks. The following methods were used: the fractal analysis, scaling methods, methods of approximation. The following results were achieved: the results of the time scale selection for constructing adequate models of modern traffic were presented. The use of such models, in particular, enables studying the dynamics of the queues of active network devices, which is important for planning and distributing the network load. The use of statistical characteristics of traffic on a small number of time scales enables expanding theoretical concepts for critical time scales, which makes this approach applicable to any traffic process including the long-term traffic. In addition, the issues of describing the behaviour of queue tails for modern high-speed computer networks are considered and the properties of the proposed model approximations are determined. The analysis of the independent Gaussian model of a wavelet domain and the multifractal wavelet model showed the advantage of the first one for the fractal traffic and a slight discrepancy in the results for traffic close to the Gaussian one. Conclusions . Various approaches to the selection of time scales used in the study of the organization of queues of modern high-speed data networks were studied. The effect of the necessary accuracy and computational power required for calculating the maximum approximation were analyzed and it was established that exponential time scales are optimal for the fractal traffic. The impact of the tails of distributions in different time scales on the process of queue organization was also shown. It was noted that in the context of non-Gaussian traffic scenarios, the correlation structure (both short-term and long-term ones) does not describe the queues behaviour adequately enough.

A matter of timing: how temporal scale selection influences cetacean ecological niche modelling

Modelling in the marine environment faces unique challenges that place greater emphasis on model accuracy. The spatio-temporal variability of this environment presents challenges when trying to develop useful habitat models. We tested how different temporal scales influence model predictions for cetaceans with different ecological requirements. We used 7 years of (opportunistic) whale watching data (>16000 cetacean sightings) collected in the Azores archipelago under the MONICET platform. We modelled the distribution of 10 cetacean species with a sampling bias correction. Distribution modelling was performed at 2 spatial scales (2 and 4 km) and 2 temporal resolutions (8 d vs. monthly averages). We used a MAXENT analysis with 3 different validation procedures. Generally, the 8 d means produced better results. In some cases (e.g. baleen whales), predictions using monthly means were no better than null models. Finer temporal grains provided essential insights, especially for species influenced by dynamic variables (e.g. sea surface temperature). For species more influenced by static variables (e.g. bathymetry), differences between temporal scales were smaller. The selection of the right temporal scale can be essential when modelling the niches of cetaceans. Datasets with high temporal resolution (e.g. whale watching data) can provide an excellent basis for these analyses, allowing use of finer temporal grains. Our models showed good predictive performance; however, limitations related to the spatial coverage were found. Merging datasets with different temporal and spatial resolutions could help to improve niche estimates. Models with better predictive capacity and transferability are needed to implement more efficient protection and conservation measures.

Ecological neighborhoods as a framework for umbrella species selection

Umbrella species are typically chosen because they are expected to confer protection for other species assumed to have similar ecological requirements. Despite its popularity and substantial history, the value of the umbrella species concept has come into question because umbrella species chosen using heuristic methods, such as body or home range size, are not acting as adequate proxies for the metrics of interest: species richness or population abundance in a multi-species community for which protection is sought. How species associate with habitat across ecological scales has important implications for understanding population size and species richness, and therefore may be a better proxy for choosing an umbrella species. We determined the spatial scales of ecological neighborhoods important for predicting abundance of 8 potential umbrella species breeding in Nebraska using Bayesian latent indicator scale selection in N-mixture models accounting for imperfect detection. We compare the conservation value measured as collective avian abundance under different umbrella species selected following commonly used criteria and selected based on identifying spatial land cover characteristics within ecological neighborhoods that maximize collective abundance. Using traditional criteria to select an umbrella species resulted in sub-maximal expected collective abundance in 86% of cases compared to selecting an umbrella species based on land cover characteristics that maximized collective abundance directly. We conclude that directly assessing the expected quantitative outcomes, rather than ecological proxies, is likely the most efficient method to maximize the potential for conservation success under the umbrella species concept.

Predicting species-habitat relationships: does body size matter?

Allometric scaling laws are foundational to structuring processes from cellular to ecosystem levels. The idea that allometric relationships underlie species characteristic selection scales, the spatial scales at which species respond to landscape features, has recently been investigated, however, supporting empirical evidence is scarce. Lack of pattern can be explained by inaccurate estimation, low power, confounding factors, or absence of a relationship. In this paper, we evaluate the relationship between body size and species characteristic selection scales after overcoming limitations of previous study designs. We conducted 1328 avian point counts across the state of Nebraska using the robust sampling design to account for imperfect detection. We used Bayesian latent indicator scale selection with N-mixture models to estimate species’ characteristic selection scales of six habitat features for 86 species. We propagated the uncertainty associated with assigning characteristic scales to a model of the relationship between body size and characteristic spatial scales. Species characteristic scales varied across habitat predictors, and varied in the uncertainty associated with selecting single characteristic scales. After propagating uncertainty our results do not support a relationship between species’ body size and the spatial scales at which they respond to landscape features. As species abundance integrates birth, death, immigration, and emigration processes, each of which are influenced by ecological processes manifesting at various scales, we question whether a general allometric relationship should be expected. Our results suggest that selection may act on responses to specific environmental features, rather than responses to spatial scale per se.

Local adaptation through genetic differentiation in highly fragmented Tilia cordata populations.

We assessed the level of geographic differentiation of Tilia cordata in Denmark based on tests of 91 trees selected from 12 isolated populations. We used quantitative analysis of spring phenology and population genetic analysis based on SSR markers to infer the likely historical genetic processes within and among populations. High genetic variation within and among populations was observed in spring phenology, which correlated with spring temperatures at the origin of the tested T. cordata trees. The population genetic analysis revealed significant differentiation among the populations, but with no clear sign of isolation by distance. We infer the findings as indications of ongoing fine scale selection in favor of local growth conditions made possible by limited gene flow among the small and fragmented populations. This hypothesis fits well with reports of limited fruiting in the investigated Danish T. cordata populations, while the species is known for its ability to propagate vegetatively by root suckers. Our results suggest that both divergent selection and genetic drift may have played important roles in forming the genetic patterns of T. cordata at its northern distribution limit. However, we also speculate that epigenetic mechanism arising from the original population environment could have created similar patterns in regulating the spring phenology.

A Sequential Non-Parametric Multivariate Two-Sample Test

Given samples from two distributions, a non-parametric two-sample test aims at determining whether the two distributions are equal or not, based on a test statistic. Classically, this statistic is computed on the whole data set, or is computed on a subset of the data set by a function trained on its complement. We consider methods in a third tier, so as to deal with large (possibly infinite) data sets, and to automatically determine the most relevant scales to work at, making two contributions. First, we develop a generic sequential non-parametric testing framework, in which the sample size need not be fixed in advance. This makes our test a truly sequential non-parametric multivariate two-sample test. Under information theoretic conditions qualifying the difference between the tested distributions, consistency of the two-sample test is established. Second, we instantiate our framework using nearest neighbor regressors, and show how the power of the resulting two-sample test can be improved using Bayesian mixtures and switch distributions. This combination of techniques yields automatic scale selection, and experiments performed on challenging data sets show that our sequential tests exhibit comparable performances to those of state-of-the-art non-sequential tests.

Scale Selection and Use in Country Image Research

Ülke imajı belirli bir ülke hakkındaki tanımlayıcı, çıkarımsal ve bilgilendirici inançların toplamı olarak tanımlanır (Martin ve Eroğlu; 1993:193). Tüketicilerin ürün kalitesine yönelik değerlendirmelerini etkilediği için de uluslararası pazarlama literatüründe çokça çalışılan bir araştırma alanıdır. Bugüne kadar yapılan çalışmaların sahip olduğu araştırma tasarımlarının ve kullanılan ölçeklerin ülke imajının boyutları çerçevesinde çok büyük bir çeşitlilik arz ettiği görülmektedir (örn. Balabanis vd., 1996; Laroche vd., 2005; Papadopoulos ve Heslop 1993; Roth ve Romeo, 1992). Bu çeşitlilik içerisinde yaşanan kavramsal karmaşa ve ülke imajı bileşenlerini ölçen ölçekler çerçevesinde gözlemlenen geçişkenlik, önemli bir sorun olarak araştırmacıların karşısında durmaktadır (Roth ve Diamantopoulos, 2009). Yapılan çalışmada öncelikle ülke imajı araştırmalarında ortaya çıkan kavram karmaşasını giderecek tanımlamalar yapılarak kavramsal çerçeve çizilmeye çalışılmıştır. Bunun yanında ülke imajı bileşenlerinin ölçümü ile ürün-ülke imajını ölçen ölçeklerin kullanımı konusunda yapılan hatalar ve bunlara getirilen eleştiriler derlenmiştir. Araştırmada özellikle genel ülke imajı ile ürün-ülke imajının birbirinden ayrıştırılarak çerçevesinin tam olarak çizilmesi gerektiği vurgulanmış ve birincisinin ikincisi üzerinde yaptığı etkiler açıklanmıştır.

EIGENENTROPY BASED CONVOLUTIONAL NEURAL NETWORK BASED ALS POINT CLOUDS CLASSIFICATION METHOD

Abstract. The classification of point clouds is the first step in the extraction of various types of geo-information form point clouds. Recently the ISPRS WG II/4 provides a benchmark on 3D semantic labelling, a convolutional neural network based method achieves the best overall accuracy performance in all participants who only use the geometrical and waveform based features extracted from the ALS data. Features of the point are calculated in different scales to achieve the best performance. It is not efficiency for the future use. In this paper, we use an eigenentropy based scale selection strategy to improve this method. The scale selection strategy improves the average F1 score and makes the classification method more simple and efficient.

Relative asynchronous index: a new measure for time series irreversibility

In this paper, we suggest a new measure for testing reversibility of time series which combines two different tools: the visibility algorithm and the inversion number. First, the visibility algorithm maps the time series to the network according to a geometric criterion. After that, the degree of irreversibility of the time series can be estimated by the relative asynchronous index (RAI), based on the inverse number, between out and \(\hbox {out}^*\) degree sequences of the network (out and \(\hbox {out}^*\) represent the outgoing sequence of forward time series and reverse time series, respectively). This method does not need to rely on additional parameters, so it can avoid the error caused by parameter estimation. In addition, we also study the multiscale RAI and find that the optimal scale selection for detection time irreversibility is 1–4. Different types of time series are used to confirm the validity of this metric. Finally, we apply the method to financial time series and find that the financial crisis can be detected by RAI.

Making more out of pixel-level change information: using a neighbourhood approach to improve land change characterization across large and heterogeneous areas

The paper demonstrates two issues; (i) how a ‘moving window approach’, that translates pixel level detected changes to landscape level, can be implemented; (ii) how the approach can overcome the limitations of pixel level change information to characterize change over large areas. First we detected changes from two periods (1986 and 2010) of LULC maps. On the pixel-based changes, we ran focal statistics summation operator separately for selected window sizes (1–10 km). Further, we assessed effect of scale in depicting the pattern and amount of change. The approach is found useful to overcome major shortfalls of pixel-based change characterization. However, varying scale of analysis provide varying amount of change and differently represent change patterns. Thus, implementing the approach over complex and large areas requires multi-scale approach. Subdividing complex and large areas into homogeneous zones can help to implement the multi-scale approach and facilitate the selection of appropriate scale of analysis.

A Saliency-Based Band Selection Approach for Hyperspectral Imagery Inspired by Scale Selection

This letter presents a band selection method relying on saliency bands and scale selection (SBSS). The SBSS method is used to excavate the hidden information of hyperspectral images effectively, while its underlying assumptions are: 1) it is reasonable to combine spectral and spatial information to excavate the intrinsic property of a hyperspectral image; 2) there are some saliency bands that can represent a hyperspectral image without significant information loss in data exploitation; and 3) saliency, scale, and image description have an intrinsic connection. The computational complexity of the SBSS method is linear, and experimental results demonstrate that the proposed method obtains competitively good results compared with other state-of-the-art band selection techniques, in terms of classification accuracy.

Survey of the current prevention status of hospital acquired venous thromboembolism in Shandong province

Objective To investigate the current prevention status of hospital acquired venous thromboembolism(VTE) in Shandong province, to further improve the prevention and control of VTE system, and provide reference for prevention of hospital care to improve the quality of VTE. Methods An electronic questionnaire survey was conducted among nurses in 81 hospitals of Shandong province. The questionnaire mainly included three parts: basic information, the current prevention status of hospital acquired VTE and the training needs of nurses. A total of 3 766 valid questionnaires were recovered. Results 59.26%(48/81) hospitals established multi-discipinary team to prevent VTE, 76.00% (38/50) of the tertiary hospitals established multi-discipinary team, which was higher than 32.26% (10/31) of the secondary hospitals, the difference was statistically significant (χ2=15.166, P=0.000). The VTE risk assessment scale had been routinely used to assess the risk of VTE in hospitalized patients, 80.37% (352/438) tertiary hospital departments implemented grading nursing care while 71.43% (175/245) secondary hospitals departments implemented grading nursing care, the difference was statistically significant(χ2=7.120, P=0.008). Each level hospital mechanical preventive equipment configuration rate was less than 70%. Training needs of nurses: 92.11% (3 469/3 766) nurses believed that it was necessary to organize the VTE related knowledge training for nurses, but there were significant differences in the training of nurses with different professional titles (Z=-12.607, P=0.000). 48.04%(1 149/2 392) nurses with junior grade professional titles were not trained. Conclusions The hospital attaches great importance to the prevention of hospital acquired VTE, but the construction level of VTE prevention system is unevenness, and primary hospitals should be further reinforced. The risk assessment scale selection lacks of uniform standards, mechanical preventive equipment allocation rate should to be further improved. And the training of primary nurses should be further strengthened. Key words: Venous thromboembolism; Questionnaires; Prevention

A fractal and entropy-based model for selecting the optimum spatial scale of soil erosion

Soil erosion is an important issue of global climate change, which directly relates to the ecological environment quality and social sustainable development. Scale dependence is an intrinsic property of geographical phenomena and processes; therefore, it is scientifically significant to select an appropriate research scale for studying the driving mechanism and the ecological environment effect of soil erosion. The optimum scale selection model based on fractal and entropy theory was built in this paper. The model firstly used the entropy index from gray level co-occurrence matrix to evaluate the data redundancy, and adopted the similarity of Area-Weighed Mean Patch Fractal Dimension (AWMFD) to assess the expression ability of geographical intrinsic features. Then the correlation between each index and scale was computed, and finally the ratio of the correlation to their summation was taken as weight to calculate the weighted summation. In this paper, Danjiangkou reservoir area was taken as the case study. The results of the experiment show that the optimum scale for studying soil erosion in Danjiangkou reservoir area is 90 m, which means under this scale the optimum balance between data redundancy and the expression ability of intrinsic features is approached. Scalograms of landscape metrics were used to verify the rationality and feasibility of the optimum scale selection model.