scholarly journals Evaluation of Clustering Methods in Compression of Topological Models and Visual Place Recognition Using Global Appearance Descriptors

2019 ◽  
Vol 9 (3) ◽  
pp. 377 ◽  
Author(s):  
Sergio Cebollada ◽  
Luis Payá ◽  
Walterio Mayol ◽  
Oscar Reinoso
Keyword(s):  
Visual Information ◽  
Clustering Algorithms ◽  
Computation Time ◽  
Operating Conditions ◽  
Compact Model ◽  
Indoor Environments ◽  
Clustering Methods ◽  
Omnidirectional Images ◽  
Robot Position ◽  
Topological Models

This paper presents an extended study about the compression of topological models of indoor environments. The performance of two clustering methods is tested in order to know their utility both to build a model of the environment and to solve the localization task. Omnidirectional images are used to create the compact model, as well as to estimate the robot position within the environment. These images are characterized through global appearance descriptors, since they constitute a straightforward mechanism to build a compact model and estimate the robot position. To evaluate the goodness of the proposed clustering algorithms, several datasets are considered. They are composed of either panoramic or omnidirectional images captured in several environments, under real operating conditions. The results confirm that compression of visual information contributes to a more efficient localization process through saving computation time and keeping a relatively good accuracy.

scholarly journals Time Efficient Unmanned Aircraft Systems Deployment in Disaster Scenarios Using Clustering Methods and a Set Cover Approach

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 422
Author(s):  
Donald Mahoro Ntwari ◽  
Daniel Gutierrez-Reina ◽  
Sergio Luis Toral Marín ◽  
Hissam Tawfik
Keyword(s):  
Clustering Algorithms ◽  
Research Work ◽  
Computation Time ◽  
Search Space ◽  
Unmanned Aircraft ◽  
Set Cover ◽  
Connected Components ◽  
Clustering Methods ◽  
Minimum Number ◽  
Aircraft System

Unmanned aircraft, which are more commonly known as drones, are nowadays extensively used in an ever increasing set of applications. In a wider system, the aircraft are usually associated to additional elements such as ground-based controllers. Furthermore, when these components form a network of elements that can communicate, the system is said to form an Unmanned Aircraft System (UAS). This system is particularly effective when the aircraft within are organized into swarms with sets of objectives to accomplish. The extensive use of swarms into UASs is more and more exploited nowadays due to the decreasing cost of those aircraft. In the present work we are interested in a particular application of UASs, namely their deployment in disaster scenarios for communications services provision to targets on the ground. These ground targets, however, are not part of the UASs and should not be confused with ground-based controllers. The present work does not only focus on coverage for ground targets but also on a guaranteed minimum number of covers for each target, which is called the redundancy requirement. The research work also ensures that the deployed UAS forms a unique connected component so that a steady stream of communication is kept with the targets to cover. Research work similar to the present perform the initial deployment of their aircraft in a different manner, either randomly, based on a predetermined grid formation, or using other elaborated methods. This work proposes a new solution based on the use of clustering algorithms, combined to a design of the problem formulated as a set cover optimization model. The clustering phase is used to discretize the search space and ease the optimization phase by locating regions of interest, and then a further procedure is applied, only when needed, to reconnect scattered connected components and guarantee connectivity in the networks. This way of doing it has achieved a deployment of UASs with maximum coverage for all targets, a guaranteed minimum number of covers for each of them, and results in a competitive computation time. The latter also allowed for more scalability by extending the tests to very large input instances.

Mobile Robot Playback Navigation Based on Robot Pose Calculation Using Memorized Omnidirectional Images

2002 ◽  
Vol 14 (4) ◽  
pp. 366-374 ◽  
Author(s):  
Lixin Tang ◽  
◽  
Shin'ichi Yuta
Keyword(s):  
Autonomous Navigation ◽  
Target Position ◽  
Manual Control ◽  
Time Interval ◽  
Indoor Environments ◽  
Evaluation Function ◽  
Omnidirectional Camera ◽  
Environmental Image ◽  
Omnidirectional Images ◽  
Robot Position

We propose a method of autonomous navigation for mobile robots in indoor environments by a teaching and playback scheme. During teaching, an operator guides a robot to move by manual control. While moving, the robot memorizes its motion measured by odometry and an environmental image taken by an omnidirectional camera at each time interval, and regards places where images were taken as target positions. When navigating autonomously, the robot plays back memorized motion to track each target position and corrects its position by calculating its relative pose using current and memorized images, to follow the taught route. In this method, vertical edges existing in the environment are used as landmarks to calculate robot position, and an evaluation function defined by us is used to find corresponding vertical edges between two images. The robot thus can navigate robustly in real building environments. The system can avoid the problem of the operator covering a part of the environment in images during the teaching stage.

scholarly journals Audiovisual Bimodal and Interactive Effects for Soundscape Design of the Indoor Environments: A Systematic Review

2021 ◽  
Vol 13 (1) ◽  
pp. 339
Author(s):  
Yoshimi Hasegawa ◽  
Siu-Kit Lau
Keyword(s):  
Systematic Review ◽  
Visual Information ◽  
Web Of Science ◽  
Interactive Effects ◽  
Indoor Environments ◽  
Sound Sources ◽  
Noise Annoyance ◽  
Visual Variable ◽  
Soundscape Studies ◽  
Potential Use

A growing number of soundscape studies involving audiovisual factors have been conducted; however, their bimodal and interactive effects on indoor soundscape evaluations have not yet been thoroughly reviewed. The overarching goal of this systematic review was to develop the framework for designing sustainable indoor soundscapes by focusing on audiovisual factors and relations. A search for individual studies was conducted through three databases and search engines: Scopus, Web of Science, and PubMed. Based on the qualitative reviews of the selected thirty papers, a framework of indoor soundscape evaluation concerning visual and audiovisual indicators was proposed. Overall, the greenery factor was the most important visual variable, followed by the water features and moderating noise annoyance perceived by occupants in given indoor environments. The presence of visual information and sound-source visibility would moderate perceived noise annoyance and influence other audio-related perceptions. Furthermore, sound sources would impact multiple perceptual responses (audio, visual, cognitive, and emotional perceptions) related to the overall soundscape experiences when certain visual factors are interactively involved. The proposed framework highlights the potential use of the bimodality and interactivity of the audiovisual factors for designing indoor sound environments in more effective ways.

scholarly journals CONCURRENT MAP BUILDING AND LOCALIZATION ON INDOOR DYNAMIC ENVIRONMENTS

2002 ◽  
Vol 16 (03) ◽  
pp. 361-374 ◽  
Author(s):  
JUAN ANDRADE-CETTO ◽  
ALBERTO SANFELIU
Keyword(s):  
Learning Rule ◽  
Dynamic Environments ◽  
Position Estimation ◽  
Indoor Environments ◽  
Map Building ◽  
Robust Learning ◽  
Map Construction ◽  
Map Updating ◽  
Dynamic Map ◽  
Robot Position

A system that builds and maintains a dynamic map for a mobile robot is presented. A learning rule associated to each observed landmark is used to compute its robustness. The position of the robot during map construction is estimated by combining sensor readings, motion commands, and the current map state by means of an Extended Kalman Filter. The combination of landmark strength validation and Kalman filtering for map updating and robot position estimation allows for robust learning of moderately dynamic indoor environments.

Statistical Evaluation of Indoor Air Quality Changes after Installation of the PSD System in Seoul’s Metro

2011 ◽  
Vol 20 (1) ◽  
pp. 187-197 ◽  
Author(s):  
Min Jeong Kim ◽  
Yong Su Kim ◽  
Abtin Ataei ◽  
Jeong Tai Kim ◽  
Jung Jin Lim ◽  
...  
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Operating Conditions ◽  
Indoor Environments ◽  
Multivariate System ◽  
Metro Station ◽  
Adverse Health Effects ◽  
Subway Stations ◽  
Tele Monitoring

The purpose of this study was to evaluate changes in the concentration of air pollutants in the indoor environments, which could be caused by seasonal changes or changes in operating conditions of subway metro stations. In fact, there are many different types of pollution that can cause contamination in subway stations, and changes in operating conditions can also lead to changes in the indoor air quality (IAQ). Therefore, in order to establish a proper management of IAQ, it would be necessary to evaluate the changes in IAQ according to the changes in conditions. To do this, the present study used a multivariate analysis of variance (MANOVA). The results of testing the hypothesis proved that two groups, divided by the condition of a platform screen door (PSD) system, could differ statistically. Furthermore, those multidimensional differences were caused by installation of a PSD system. When applied to a real-time tele-monitoring system, MANOVA could clearly identify the daily and weekly variations of IAQ in the subway station, as well as the PSD system’s condition. Accordingly, this method could be useful for developing a multivariate system to statistically evaluate the experimental IAQ results in order to optimise operating conditions in a subway metro station to improve IAQ, and to minimise adverse health effects on passengers by exposure to harmful substances.

Prediction of Hot Aisle Partition Airflow Boundary Conditions

2013 ◽  
Author(s):  
Zhihang Song ◽  
Bruce T. Murray ◽  
Bahgat Sammakia
Keyword(s):  
Boundary Conditions ◽  
Data Center ◽  
Computation Time ◽  
Design Tool ◽  
Operating Conditions ◽  
Ann Model ◽  
Flow Rates ◽  
Level Model ◽  
Optimization Methodology ◽  
Artificial Neural Network Ann

The integration of a simulation-based Artificial Neural Network (ANN) with a Genetic Algorithm (GA) has been explored as a real-time design tool for data center thermal management. The computation time for the ANN-GA approach is significantly smaller compared to a fully CFD-based optimization methodology for predicting data center operating conditions. However, difficulties remain when applying the ANN model for predicting operating conditions for configurations outside of the geometry used for the training set. One potential remedy is to partition the room layout into a finite number of characteristic zones, for which the ANN-GA model readily applies. Here, a multiple hot aisle/cold aisle data center configuration was analyzed using the commercial software FloTHERM. The CFD results are used to characterize the flow rates at the inter-zonal partitions. Based on specific reduced subsets of desired treatment quantities from the CFD results, such as CRAC and server rack air flow rates, the approach was applied for two different CRAC configurations and various levels of CRAC and server rack flow rates. Utilizing the compact inter-zonal boundary conditions, good agreement for the airflow and temperature distributions is achieved between predictions from the CFD computations for the entire room configuration and the reduced order zone-level model for different operating conditions and room layouts.

scholarly journals ENHANCED RGB-D MAPPING METHOD FOR DETAILED 3D MODELING OF LARGE INDOOR ENVIRONMENTS

2016 ◽  
Vol III-1 ◽  
pp. 151-158
Author(s):  
Shengjun Tang ◽  
Qing Zhu ◽  
Wu Chen ◽  
Walid Darwish ◽  
Bo Wu ◽  
...  
Keyword(s):  
3D Modeling ◽  
Visual Information ◽  
3D Models ◽  
Measurement Range ◽  
Mapping Method ◽  
Depth Information ◽  
Indoor Environments ◽  
Recovery Method ◽  
Depth Measurement ◽  
Rgb Image

RGB-D sensors are novel sensing systems that capture RGB images along with pixel-wise depth information. Although they are widely used in various applications, RGB-D sensors have significant drawbacks with respect to 3D dense mapping of indoor environments. First, they only allow a measurement range with a limited distance (e.g., within 3 m) and a limited field of view. Second, the error of the depth measurement increases with increasing distance to the sensor. In this paper, we propose an enhanced RGB-D mapping method for detailed 3D modeling of large indoor environments by combining RGB image-based modeling and depth-based modeling. The scale ambiguity problem during the pose estimation with RGB image sequences can be resolved by integrating the information from the depth and visual information provided by the proposed system. A robust rigid-transformation recovery method is developed to register the RGB image-based and depth-based 3D models together. The proposed method is examined with two datasets collected in indoor environments for which the experimental results demonstrate the feasibility and robustness of the proposed method

scholarly journals RFCell: A Gene Selection Approach for scRNA-seq Clustering Based on Permutation and Random Forest

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuan Zhao ◽  
Zhao-Yu Fang ◽  
Cui-Xiang Lin ◽  
Chao Deng ◽  
Yun-Pei Xu ◽  
...  
Keyword(s):  
Random Forest ◽  
Single Cell ◽  
Gene Selection ◽  
Clustering Algorithms ◽  
Selection Methods ◽  
Clustering Methods ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Random Forest Classification ◽  
Forest Classification

In recent years, the application of single cell RNA-seq (scRNA-seq) has become more and more popular in fields such as biology and medical research. Analyzing scRNA-seq data can discover complex cell populations and infer single-cell trajectories in cell development. Clustering is one of the most important methods to analyze scRNA-seq data. In this paper, we focus on improving scRNA-seq clustering through gene selection, which also reduces the dimensionality of scRNA-seq data. Studies have shown that gene selection for scRNA-seq data can improve clustering accuracy. Therefore, it is important to select genes with cell type specificity. Gene selection not only helps to reduce the dimensionality of scRNA-seq data, but also can improve cell type identification in combination with clustering methods. Here, we proposed RFCell, a supervised gene selection method, which is based on permutation and random forest classification. We first use RFCell and three existing gene selection methods to select gene sets on 10 scRNA-seq data sets. Then, three classical clustering algorithms are used to cluster the cells obtained by these gene selection methods. We found that the gene selection performance of RFCell was better than other gene selection methods.

scholarly journals Comparison of dimensionality reduction and clustering methods for SARS-CoV-2 genome

2021 ◽  
Vol 10 (4) ◽  
pp. 2170-2180
Author(s):  
Untari N. Wisesty ◽  
Tati Rajab Mengko
Keyword(s):  
Dimensionality Reduction ◽  
Dimensional Reduction ◽  
Clustering Algorithm ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
Gaussian Mixture Models ◽  
Reduction Process ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Clustering Methods

This paper aims to conduct an analysis of the SARS-CoV-2 genome variation was carried out by comparing the results of genome clustering using several clustering algorithms and distribution of sequence in each cluster. The clustering algorithms used are K-means, Gaussian mixture models, agglomerative hierarchical clustering, mean-shift clustering, and DBSCAN. However, the clustering algorithm has a weakness in grouping data that has very high dimensions such as genome data, so that a dimensional reduction process is needed. In this research, dimensionality reduction was carried out using principal component analysis (PCA) and autoencoder method with three models that produce 2, 10, and 50 features. The main contributions achieved were the dimensional reduction and clustering scheme of SARS-CoV-2 sequence data and the performance analysis of each experiment on each scheme and hyper parameters for each method. Based on the results of experiments conducted, PCA and DBSCAN algorithm achieve the highest silhouette score of 0.8770 with three clusters when using two features. However, dimensionality reduction using autoencoder need more iterations to converge. On the testing process with Indonesian sequence data, more than half of them enter one cluster and the rest are distributed in the other two clusters.

scholarly journals PRIVACY PRESERVING CLUSTERING BASED ON LINEAR APPROXIMATION OF FUNCTION

2013 ◽  
Vol 12 (5) ◽  
pp. 3443-3451
Author(s):  
Rajesh Pasupuleti ◽  
Narsimha Gugulothu
Keyword(s):  
Linear Approximation ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Privacy Preserving ◽  
Distance Measures ◽  
Clustering Methods ◽  
Sensitive Data ◽  
Processing Information ◽  
Data Objects ◽  
Approximation Of Function

Clustering analysis initiatives  a new direction in data mining that has major impact in various domains including machine learning, pattern recognition, image processing, information retrieval and bioinformatics. Current clustering techniques address some of the  requirements not adequately and failed in standardizing clustering algorithms to support for all real applications. Many clustering methods mostly depend on user specified parametric methods and initial seeds of clusters are randomly selected by  user.  In this paper, we proposed new clustering method based on linear approximation of function by getting over all idea of behavior knowledge of clustering function, then pick the initial seeds of clusters as the points on linear approximation line and perform clustering operations, unlike grouping data objects into clusters by using distance measures, similarity measures and statistical distributions in traditional clustering methods. We have shown experimental results as clusters based on linear approximation yields good  results in practice with an example of  business data are provided.  It also  explains privacy preserving clusters of sensitive data objects.

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