scholarly journals Scale-Dependent Point Selection Methods for Web Maps

2021 ◽  
Author(s):  
Mathias Gröbe ◽  
Dirk Burghardt
Keyword(s):  
Multiple Scales ◽  
Selection Methods ◽  
Point Selection ◽  
New Approach ◽  
Digital Maps ◽  
Numerical Attributes ◽  
Nearest Point ◽  
Grid Approach ◽  
Isolation Measure ◽  
Selection Algorithms

AbstractIn cartographic generalization, the selection is an often-used method to adjust information density in a map. This paper deals with methods for selecting point features for a specific scale with numerical attributes, such as population, elevation, or visitors. With the Label Grid approach and the method of Functional Importance, two existing approaches are described, which have not been published in the scientific literature so far. They are explained and illustrated in the method chapter for better understanding. Furthermore, a new approach based on the Discrete Isolation measure is introduced. It combines the spatial position and the attribute's value and is defined as the minimum distance to the nearest point with a higher value. All described selection methods are implemented and made available as Plugins named “Point selection algorithms” for QGIS. Based on this implementation, the three methods are compared regarding runtime, parameterization, legibility, and generalization degree. Finally, recommendations are given on which data and use cases the approaches are suitable. We see digital maps with multiple scales as the main application of those methods. The possibilities of labeling the selected points are not considered within the scope of this work.

Selection of additive manufacturing processes

2017 ◽  
Vol 23 (2) ◽  
pp. 434-447 ◽  
Author(s):  
Yuanbin Wang ◽  
Robert Blache ◽  
Xun Xu
Keyword(s):  
Additive Manufacturing ◽  
New Technologies ◽  
Rapid Development ◽  
A Priori ◽  
Design Stage ◽  
Selection Methods ◽  
Sequential Decision ◽  
Process Selection ◽  
New Approach ◽  
Content Type

Purpose This study aims to review the existing methods for additive manufacturing (AM) process selection and evaluate their suitability for design for additive manufacturing (DfAM). AM has experienced a rapid development in recent years. New technologies, machines and service bureaus are being brought into the market at an exciting rate. While user’s choices are in abundance, finding the right choice can be a non-trivial task. Design/methodology/approach AM process selection methods are reviewed based on decision theory. The authors also examine how the user’s preferences and AM process performances are considered and approximated into mathematical models. The pros and cons and the limitations of these methods are discussed, and a new approach has been proposed to support the iterating process of DfAM. Findings All current studies follow a sequential decision process and focus on an “a priori” articulation of preferences approach. This kind of method has limitations for the user in the early design stage to implement the DfAM process. An “a posteriori” articulation of preferences approach is proposed to support DfAM and an iterative design process. Originality/value This paper reviews AM process selection methods in a new perspective. The users need to be aware of the underlying assumptions in these methods. The limitations of these methods for DfAM are discussed, and a new approach for AM process selection is proposed.

scholarly journals CUR: Group Profiling with Community-based Users’ Representation

2018 ◽  
Author(s):  
João Emanoel Ambrósio Gomes ◽  
Ricardo B. C. Prudêncio ◽  
André C. A. Nascimento
Keyword(s):  
Feature Selection ◽  
Relevant Feature ◽  
Selection Methods ◽  
Community Based ◽  
Feature Sets ◽  
Network Community ◽  
Representation Method ◽  
Selection Algorithms ◽  
Content Information ◽  
Better Than

Group profiling methods aim to construct a descriptive profile for communities in social networks. Before the application of a profiling algorithm, it is necessary to collect and preprocess the users’ content information, i.e., to build a representation of each user in the network. Usually, existing group profiling strategies define the users’ representation by uniformly processing the entire content information in the network, and then, apply traditional feature selection methods over the user features in a group. However, such strategy may ignore specific characteristics of each group. This fact can lead to a limited representation for some communities, disregarding attributes which are relevant to the network perspective and describing more clearly a particular community despite the others. In this context, we propose the community-based user’s representation method (CUR). In this proposal, feature selection algorithms are applied over user features for each network community individually, aiming to assign relevant feature sets for each particular community. Such strategy will avoid the bias caused by larger communities on the overall user representation. Experiments were conducted in a co-authorship network to evaluate the CUR representation on different group profiling strategies and were assessed by hu- man evaluators. The results showed that profiles obtained after the application of the CUR module were better than the ones obtained by conventional users’ representation on an average of 76.54% of the evaluations.

scholarly journals DISGROU: an algorithm for discontinuous subgroup discovery

2021 ◽  
Vol 7 ◽  
pp. e512
Author(s):  
Reynald Eugenie ◽  
Erick Stattner
Keyword(s):  
Basic Assumption ◽  
Numerical Data ◽  
Subgroup Discovery ◽  
Search Process ◽  
Target Variable ◽  
Traditional Methods ◽  
New Approach ◽  
Numerical Attributes

In this paper, we focus on the problem of the search for subgroups in numerical data. This approach aims to identify the subsets of objects, called subgroups, which exhibit interesting characteristics compared to the average, according to a quality measure calculated on a target variable. In this article, we present DISGROU, a new approach that identifies subgroups whose attribute intervals may be discontinuous. Unlike the main algorithms in the field, the originality of our proposal lies in the way it breaks down the intervals of the attributes during the subgroup research phase. The basic assumption of our approach is that the range of attributes defining the groups can be disjoint to improve the quality of the identified subgroups. Indeed the traditional methods in the field perform the subgroup search process only over continuous intervals, which results in the identification of subgroups defined over wider intervals thus containing some irrelevant objects that degrade the quality function. In this way, another advantage of our approach is that it does not require a prior discretization of the attributes, since it works directly on the numerical attributes. The efficiency of our proposal is first demonstrated by comparing the results with two algorithms that are references in the field and then by applying to a case study.

P–173 Predicting implantation: comparative performance of two embryo selection algorithms and conventional morphological grading after frozen blastocyst transfer

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
S Biswa. Shivhare ◽  
A Price ◽  
S Ingamells
Keyword(s):  
Prediction Models ◽  
Predictive Performance ◽  
Time Lapse ◽  
Embryo Selection ◽  
Blastocyst Transfer ◽  
Selection Methods ◽  
Single Centre ◽  
Centre Study ◽  
Single Centre Study ◽  
Selection Algorithms

Abstract Study question What is the predictive performance of iDAScore® compared with KIDScore™D5 and conventional morphology grading for implantation after frozen blastocyst transfer? Summary answer iDAScore®, KIDScore™D5 models and conventional morphology grading are all significantly associated with implantation after frozen blastocyst transfer. However, their predictive performance remains fair. What is known already Embryo selection algorithms (ESA) have been used in conjunction with conventional morphology grading (CMG) to rank embryo quality in an attempt to optimise embryo selection prior to transfer. Traditionally, ESA, such as the KIDScore™D5 prediction model, have been based on predetermined morphokinetic parameters which vary according to the specific ESA used. Recently, algorithms based on artificial intelligence (AI) deep learning such as the iDAScore®, have been developed using raw time lapse image data of embryos with known implantation outcome. Embryo scores generated by both traditional and AI based ESA have been positively correlated with implantation potential in retrospective studies. Study design, size, duration This retrospective single centre study included data from 157 frozen embryo transfers carried out between January 2020 and January 2021 with embryos cultured in EmbryoScope+ time-lapse incubator. Embryos were selected for transfer using CMG. iDAScore® and KIDScore™D5 were generated retrospectively for each embryo transferred. Sensitivity and specificity for implantation in addition to concordance was determined for all three embryo scoring systems and compared. Participants/materials, setting, methods Statistical analysis was performed with SPSS software. ROC curve analysis was performed to investigate the predictive performance of the three embryo selection methods for implantation. Chi-Square test was used to determine if there was a significant association between iDAScore®, KIDScore™D5 and CMG with implantation. Spearman’s correlation tested for correlation between iDAScore®, KIDScore™D5 and CMG. Main results and the role of chance A statistically significant but limited predictive power was observed between iDAScore®, KIDScore™D5 and CMG methods of embryo selection and implantation rate (AUC=0.675, 95% CI 0.59, 0.76; AUC=0.683, 95% CI 0.60, 0.77 and AUC=0.638, 95% CI 0.55, 0.73) respectively. Unsurprisingly, higher values for each of iDAScore®, KIDScore™D5 and CMG were significantly associated with higher implantation (p = 0.002, p < 0.001, p = 0.003) respectively. Interestingly, there was a significantly moderate correlation between iDAScore® and CMG rs (155)=0.581, p < 0.001, while a significantly strong correlation between KIDScore™D5 and CMG rs (155)=0.775, p < 0.001 as well as between iDAScore® and KIDScore™D5 rs (155)=0.799, p < 0.001. Limitations, reasons for caution This is a retrospective single centre study with limited sample size, hence the results may be interpreted with caution. Further analysis involving a larger patient cohort must be carried out prior to implementing iDAScore® or KIDScore™D5 as primary embryo selection methods. Wider implications of the findings: The findings of this study support the potential application of iDAScore® and KIDScore™D5 prediction models in automatic embryo selection, thereby minimising operator variability and time taken for embryo grading. Trial registration number Not applicable

Analysis of the Effect on Image Quality of Different Scanning Point Selection Methods in Sparse ESM

2019 ◽  
Vol 61 (6) ◽  
pp. 1823-1831 ◽  
Author(s):  
Morten Sorensen ◽  
Hamed Kajbaf ◽  
Victor V. Khilkevich ◽  
Ling Zhang ◽  
David Pommerenke
Keyword(s):  
Image Quality ◽  
Selection Methods ◽  
Point Selection
Sign in / Sign up

Export Citation Format

Share Document