scholarly journals A SLIC-DBSCAN Based Algorithm for Extracting Effective Sky Region from a Single Star Image

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5786
Author(s):  
Chenguang Shi ◽  
Rui Zhang ◽  
Yong Yu ◽  
Xingzhe Sun ◽  
Xiaodong Lin
Keyword(s):  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Saliency Map ◽  
High Accuracy ◽  
Star Tracker ◽  
Single Image ◽  
Large Area ◽  
Single Star ◽  
Star Image ◽  
Simple Linear Iterative Clustering

The star tracker is widely used for high-accuracy missions due to its high accuracy position high autonomy and low power consumption. On the other hand, the ability of interference suppression of the star tracker has always been a hot issue of concern. A SLIC-DBSCAN-based algorithm for extracting effective information from a single image with strong interference has been developed in this paper to remove interferences. Firstly, the restricted LC (luminance-based contrast) transformation is utilized to enhance the contrast between background noise and the large-area interference. Then, SLIC (the simple linear iterative clustering) algorithm is adopted to segment the saliency map and in this process, optimized parameters are harnessed. Finally, from these segments, features are extracted and superpixels with similar features are combined by using DBSCAN (density-based spatial clustering of applications with noise). The proposed algorithm is proved effective by successfully removing large-area interference and extracting star spots from the sky region of the real star image.

scholarly journals A High Accuracy Time-Reversal Based WiFi Indoor Localization Approach with a Single Antenna

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3437 ◽  
Author(s):  
Lili Zheng ◽  
Binjie Hu ◽  
Haoxiang Chen
Keyword(s):  
Time Reversal ◽  
Clustering Algorithm ◽  
Indoor Localization ◽  
Spatial Clustering ◽  
High Accuracy ◽  
Optimal Number ◽  
Communication Link ◽  
Limited Bandwidth ◽  
Single Antenna ◽  
Localization Approach

In this paper, we study the influence of multipath magnitude, bandwidth, and communication link number on the performance of the existing time-reversal (TR) based fingerprinting localization approach and find that the localization accuracy deteriorates with a limited bandwidth. To improve the localization performance, by exploiting two unique location-specified signatures extracted from Channel State Information (CSI), we propose a high accuracy TR fingerprint localization approach, HATRFLA. Furthermore, we employ a density-based spatial clustering algorithm to minimize the storage space of the fingerprint database by adaptively selecting the optimal number of fingerprints for each location. Experimental results confirm that the proposed approach can efficiently mitigate accuracy deterioration caused by a limited bandwidth and consequently, achieve higher accuracy compared with the existing TR localization approach.

scholarly journals An Enhanced Spectral Clustering Algorithm with S-Distance

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 596
Author(s):  
Krishna Kumar Sharma ◽  
Ayan Seal ◽  
Enrique Herrera-Viedma ◽  
Ondrej Krejcar
Keyword(s):  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Rank Test ◽  
Customer Churn ◽  
Signed Rank ◽  
Signed Rank Test ◽  
Spectral Clustering Algorithm ◽  
Industrial Databases

Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering for predicting churn with better accuracy by analyzing industrial data. The linear Euclidean distance in the traditional SC is replaced by the non-linear S-distance (Sd). The Sd is deduced from the concept of S-divergence (SD). Several characteristics of Sd are discussed in this work. Assays are conducted to endorse the proposed clustering algorithm on four synthetics, eight UCI, two industrial databases and one telecommunications database related to customer churn. Three existing clustering algorithms—k-means, density-based spatial clustering of applications with noise and conventional SC—are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of its Jaccard index, f-score, recall, precision and accuracy. Finally, we also test the significance of the clustering results by the Wilcoxon’s signed-rank test, Wilcoxon’s rank-sum test, and sign tests. The relative study shows that the outcomes of the proposed algorithm are interesting, especially in the case of clusters of arbitrary shape.

Algorithm Design and Implementation of Dynamic Star Simulator

2013 ◽  
Vol 722 ◽  
pp. 187-193
Author(s):  
Xiao Fang Zhao ◽  
Guang Bin Liu ◽  
Chao Shan Liu
Keyword(s):  
Traditional Method ◽  
Algorithm Design ◽  
Star Tracker ◽  
Simulation Algorithm ◽  
Arc Length ◽  
Visual Axis ◽  
Star Image ◽  
Design And Implementation ◽  
Simulation Testing ◽  
Celestial Sphere

In order to realize the simulation testing and calibration of star tracker in the lab, a simulation algorithm for dynamic and celestial sphere star image was proposed. This algorithm could complete simulation of star image at any time and any visual axis point, also could realize in-orbit simulation according to a given track. A new method of space district dividing was also presented in order to improve the traditional method of space district dividing by ascension and declination, fully considered declination arc length gradually shortened with latitude increasing. It can remarkably enhance the updating frequency simulation star image.

Tree-ART2 Learning Model for Spatial Clustering in Second Dimension

2014 ◽  
Vol 543-547 ◽  
pp. 1934-1938
Author(s):  
Ming Xiao
Keyword(s):  
Network Model ◽  
Spatial Data ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Adaptive Resonance Theory ◽  
Spatial Distance ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Vector Module

For a clustering algorithm in two-dimension spatial data, the Adaptive Resonance Theory exists not only the shortcomings of pattern drift and vector module of information missing, but also difficultly adapts to spatial data clustering which is irregular distribution. A Tree-ART2 network model was proposed based on the above situation. It retains the memory of old model which maintains the constraint of spatial distance by learning and adjusting LTM pattern and amplitude information of vector. Meanwhile, introducing tree structure to the model can reduce the subjective requirement of vigilance parameter and decrease the occurrence of pattern mixing. It is showed that TART2 network has higher plasticity and adaptability through compared experiments.

scholarly journals An environmental dependence of the physical and structural properties in the Hydra cluster galaxies

2020 ◽  
Vol 500 (1) ◽  
pp. 1323-1339
Author(s):  
Ciria Lima-Dias ◽  
Antonela Monachesi ◽  
Sergio Torres-Flores ◽  
Arianna Cortesi ◽  
Daniel Hernández-Lang ◽  
...  
Keyword(s):  
Structural Properties ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Broad Band ◽  
Visible Region ◽  
Early Type ◽  
Cluster Galaxies ◽  
Using Data ◽  
Stellar Masses ◽  
Environmental Dependence

ABSTRACT The nearby Hydra cluster (∼50 Mpc) is an ideal laboratory to understand, in detail, the influence of the environment on the morphology and quenching of galaxies in dense environments. We study the Hydra cluster galaxies in the inner regions (1R200) of the cluster using data from the Southern Photometric Local Universe Survey, which uses 12 narrow and broad-band filters in the visible region of the spectrum. We analyse structural (Sérsic index, effective radius) and physical (colours, stellar masses, and star formation rates) properties. Based on this analysis, we find that ∼88 per cent of the Hydra cluster galaxies are quenched. Using the Dressler–Schectman test approach, we also find that the cluster shows possible substructures. Our analysis of the phase-space diagram together with density-based spatial clustering algorithm indicates that Hydra shows an additional substructure that appears to be in front of the cluster centre, which is still falling into it. Our results, thus, suggest that the Hydra cluster might not be relaxed. We analyse the median Sérsic index as a function of wavelength and find that for red [(u − r) ≥2.3] and early-type galaxies it displays a slight increase towards redder filters (13 and 18 per cent, for red and early type, respectively), whereas for blue + green [(u − r)<2.3] galaxies it remains constant. Late-type galaxies show a small decrease of the median Sérsic index towards redder filters. Also, the Sérsic index of galaxies, and thus their structural properties, do not significantly vary as a function of clustercentric distance and density within the cluster; and this is the case regardless of the filter.

scholarly journals Multi-zone prediction analysis of city-scale travel order demand

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248064
Author(s):  
Pengshun Li ◽  
Jiarui Chang ◽  
Yi Zhang ◽  
Yi Zhang
Keyword(s):  
Prediction Model ◽  
Clustering Algorithm ◽  
Intelligent Transportation System ◽  
Spatial Clustering ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Short Term ◽  
Prediction Ability ◽  
Demand Prediction ◽  
Zone Division

Taxi order demand prediction is of tremendous importance for continuous upgrading of an intelligent transportation system to realise city-scale and personalised services. An accurate short-term taxi demand prediction model in both spatial and temporal relations can assist a city pre-allocate its resources and facilitate city-scale taxi operation management in a megacity. To address problems similar to the above, in this study, we proposed a multi-zone order demand prediction model to predict short-term taxi order demand in different zones at city-scale. A two-step methodology was developed, including order zone division and multi-zone order prediction. For the zone division step, the K-means++ spatial clustering algorithm was used, and its parameter k was estimated by the between–within proportion index. For the prediction step, six methods (backpropagation neural network, support vector regression, random forest, average fusion-based method, weighted fusion-based method, and k-nearest neighbour fusion-based method) were used for comparison. To demonstrate the performance, three multi-zone weighted accuracy indictors were proposed to evaluate the order prediction ability at city-scale. These models were implemented and validated on real-world taxi order demand data from a three-month consecutive collection in Shenzhen, China. Experiment on the city-scale taxi demand data demonstrated the superior prediction performance of the multi-zone order demand prediction model with the k-nearest neighbour fusion-based method based on the proposed accuracy indicator.

scholarly journals Designation Homemade Instrument to Measure Turbidity of Barium Sulphate

2010 ◽  
Vol 7 (1) ◽  
pp. 564-572
Author(s):  
Baghdad Science Journal
Keyword(s):  
Clinical Laboratory ◽  
Volume Ratio ◽  
Barium Sulphate ◽  
High Accuracy ◽  
Weight System ◽  
Local Mode ◽  
Light Weight ◽  
Large Area ◽  
Variable Intensity ◽  
Turbidity Method

This research include the designation of newly instrument (Turbidmeter) depending on using photo voltaic detector (8.5mm.*8.5mm.).These dimensions have large area which increases the scattering rays with a variable intensity. The properties of this design are local mode and the used tools are a available in the local markets as well as its less cost light weight system. It is worth mentioning that the possibility of its application in many fields such as: Clinical, Laboratory, Industrial and Fuel fields. This designation, applied to estimate Barium Sulphate in turbidity method. The analytical results show high accuracy and repetition, also the linearity ranges from (4-180) ppm. At the detection limit (0.05) ppm. With correlation coefficient (0.9992), as well as using volume ratio percents (ethanol-glycerin) equal to (10-90) %.

Street-Level Imagery and Deep Learning for Characterization of Exposed Buildings

2021 ◽  
Author(s):  
Patrick Aravena Pelizari ◽  
Christian Geiß ◽  
Elisabeth Schoepfer ◽  
Torsten Riedlinger ◽  
Paula Aguirre ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structural Characteristics ◽  
High Accuracy ◽  
Risk Modeling ◽  
Sources Of Information ◽  
Large Area ◽  
Image Domain ◽  
Street Level ◽  
Building Characteristics

<p>Knowledge on the key structural characteristics of exposed buildings is crucial for accurate risk modeling with regard to natural hazards. In risk assessment this information is used to interlink exposed buildings with specific representative vulnerability models and is thus a prerequisite to implement sound risk models. The acquisition of such data by conventional building surveys is usually highly expensive in terms of labor, time, and money. Institutional data bases such as census or tax assessor data provide alternative sources of information. Such data, however, are often inappropriate, out-of-date, or not available. Today, the large-area availability of systematically collected street-level data due to global initiatives such as Google Street View, among others, offers new possibilities for the collection of <em>in-situ</em> data. At the same time, developments in machine learning and computer vision – in deep learning in particular – show high accuracy in solving perceptual tasks in the image domain. Thereon, we explore the potential of an automatized and thus efficient collection of vulnerability related building characteristics. To this end, we elaborated a workflow where the inference of building characteristics (e.g., the seismic building structural type, the material of the lateral load resisting system or the building height) from geotagged street-level imagery is tasked to a custom-trained Deep Convolutional Neural Network. The approach is applied and evaluated for the earthquake-prone Chilean capital Santiago de Chile. Experimental results are presented and show high accuracy in the derivation of addressed target variables. This emphasizes the potential of the proposed methodology to contribute to large-area collection of <em>in-situ</em> information on exposed buildings.</p>

An Energy-Efficient Multilevel Clustering Algorithm for Heterogeneous Wireless Sensor Networks

2011 ◽  
Vol 3 (3) ◽  
pp. 62-79
Author(s):  
Surender Soni ◽  
Vivek Katiyar ◽  
Narottam Chand
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Large Area ◽  
Topology Management ◽  
Clustering Protocol ◽  
Heterogeneous Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are generally believed to be homogeneous, but some sensor nodes of higher energy can be used to prolong the lifetime and reliability of WSNs. This gives birth to the concept of Heterogeneous Wireless Sensor Networks (HWSNs). Clustering is an important technique to prolong the lifetime of WSNs and to reduce energy consumption as well, by topology management and routing. HWSNs are popular in real deployments (Corchado et al., 2010), and have a large area of coverage. In such scenarios, for better connectivity, the need for multilevel clustering protocols arises. In this paper, the authors propose an energy-efficient protocol called heterogeneous multilevel clustering and aggregation (HMCA) for HWSNs. HMCA is simulated and compared with existing multilevel clustering protocol EEMC (Jin et al., 2008) for homogeneous WSN. Simulation results demonstrate that the proposed protocol performs better.

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