scholarly journals Improved Indoor Fingerprinting Localization Method Using Clustering Algorithm and Dynamic Compensation

2021 ◽  
Vol 10 (9) ◽  
pp. 613
Author(s):  
Jingxue Bi ◽  
Lu Huang ◽  
Hongji Cao ◽  
Guobiao Yao ◽  
Wengang Sang ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Maximum Error ◽  
Location Estimation ◽  
Reference Points ◽  
Affinity Propagation ◽  
Dynamic Compensation ◽  
Localization Method ◽  
Affinity Propagation Clustering ◽  
Hybrid Distance ◽  
Fingerprint Database

Many indoor fingerprinting localization methods are based on signal-domain distances with large localization error and low stability. An improved fingerprinting localization method using a clustering algorithm and dynamic compensation was proposed. In the offline stage, the fingerprint database was built and clustered based on offline hybrid distance and an affinity propagation clustering algorithm. Furthermore, clusters were adjusted using transition regions and a given radius, as well as updating the corresponding position and fingerprint of the cluster centroid. In the online stage, the lost received signal strength (RSS) in the reference fingerprint would be dynamically compensated by using a minimum RSS value, rather than a fixed one. Online signal-domain distance was calculated for cluster identification based on RSS readings and compensated reference fingerprint. Then, K reference points with minimum online signal-domain distances were selected, and affinity propagation clustering was reused by position-domain distances to choose the position-concentrated sub-cluster for location estimation. Experimental results show that the proposed method outperforms state-of-the-art fingerprinting methods, with the mean error of 2.328 m, the root mean square error of 1.865 m and the maximum error of 10.722 m in a testbed of 3200 square meters. The improvement rates, in terms of accuracy and stability, are more than 21% and 13%, respectively.

scholarly journals Practical Fingerprinting Localization for Indoor Positioning System by Using Beacons

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Santosh Subedi ◽  
Jae-Young Pyun
Keyword(s):  
Estimation Error ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Reference Points ◽  
Ultra Wideband ◽  
Positioning System ◽  
Localization Method ◽  
Weighted Centroid Localization ◽  
Time Required ◽  
Indoor Positioning System

Recent developments in the fields of smartphones and wireless communication technologies such as beacons, Wi-Fi, and ultra-wideband have made it possible to realize indoor positioning system (IPS) with a few meters of accuracy. In this paper, an improvement over traditional fingerprinting localization is proposed by combining it with weighted centroid localization (WCL). The proposed localization method reduces the total number of fingerprint reference points over the localization space, thus minimizing both the time required for reading radio frequency signals and the number of reference points needed during the fingerprinting learning process, which eventually makes the process less time-consuming. The proposed positioning has two major steps of operation. In the first step, we have realized fingerprinting that utilizes lightly populated reference points (RPs) and WCL individually. Using the location estimated at the first step, WCL is run again for the final location estimation. The proposed localization technique reduces the number of required fingerprint RPs by more than 40% compared to normal fingerprinting localization method with a similar localization estimation error.

Feature Selection of Sudden Failure Based on Affinity Propagation Clustering

2012 ◽  
Vol 586 ◽  
pp. 241-246
Author(s):  
Li Min Li ◽  
Zhong Sheng Wang
Keyword(s):  
Feature Selection ◽  
Clustering Algorithm ◽  
Feature Space ◽  
Failure Diagnosis ◽  
Affinity Propagation ◽  
Svm Classifier ◽  
Incorrect Answer ◽  
Sudden Failure ◽  
Affinity Propagation Clustering ◽  
Selection Of

When diagnosing sudden mechanical failure, in order to make the result of classification more accurate, in this article we describe an affinity propagation clustering algorithm for feature selection of sudden machinery failure diagnosis. General methods of feature selection select features by reducing dimension of the features, at the same time changing the data in the feature space, which would result in incorrect answer to the diagnosis. While affinity propagation method is based on measuring similarity between features whereby redundancy therein is removed, and selecting the exemplar subset of features, while doesn't change the data in the feature space. After testing on clustering and taking the result of PCA and affinity propagation clustering as input of a same SVM classifier, we get the conclusion that the latter has lower error than the former.

scholarly journals A Quantitative Site-Specific Classification Approach Based on Affinity Propagation Clustering

2020 ◽  
Author(s):  
Sayed Moustafa ◽  
Farhan Khan ◽  
Mohamed Metwaly ◽  
Eslam A.Elawadi ◽  
Nassir Al-Arifi
Keyword(s):  
Urban Areas ◽  
Clustering Algorithm ◽  
Site Response ◽  
Seismic Hazard Assessment ◽  
Spectral Ratio ◽  
Site Effect ◽  
Affinity Propagation ◽  
Site Classification ◽  
Site Specific ◽  
Affinity Propagation Clustering

Abstract Investigations made to evaluate the site effect characteristics and develop a reliable site classification scheme have received the paramount importance for the planning of urban areas and for a reliable site-specific seismic hazard assessment. This paper presents a new approach for site classification based on affinity propagation (AP) along with a selected set of representative horizontal to vertical spectral ratio (HVSR) curves inside King Saud University (KSU) campus. Measurements of the ambient vibrations were performed to cover the entire campus area by about 307 stations with 20 minutes recording length and sample rate of 128 Hz for each station to satisfy the criteria for reliable and unambiguous HVSR results. Predominant period values were used for identifying of site response and subsequent site classification. Empirical equations from the literature relating frequency of HVSR peak to average shear wave velocity in the upper 30m, commonly used as a proxy for site classification, were found to be unreliable, making site classification difficult. To overcome this problem, Affinity propagation clustering algorithm is used. The obtained results illustrated that microtremors spectral ratios can be remarkably robust tool in determining site effects. The survey results concluded to the preliminary seismic site classification map for the mapped area, which would be useful for future safe design of structures. Finally, the results presented in this study are encouraging prolongation of this type of study in other parts of Saudi Arabia using the microtremors data and site response functions.

On Expanded and Improved Affinity Propagation Clustering Algorithm

2011 ◽  
Vol 48-49 ◽  
pp. 753-756
Author(s):  
Xin Quan Chen
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Grid Cell ◽  
Space Complexity ◽  
Affinity Propagation ◽  
Data Sets ◽  
Time And Space ◽  
Affinity Propagation Clustering ◽  
Clustering Quality ◽  
Time And Space Complexity

Facing to the shortcoming of Affinity Propagation algorithm (AP), we present two expanded and improved AP algorithms. In the two algorithms, the AP algorithm based on Grid Cell (APGC) is an effective extension of AP algorithm on the level of grid cells, and the AP clustering algorithm based on Near neighbour Sampling (APNS) is trying to make some improving in time and space complexity. From some simulated comparison experiments of three algorithms, we know that APGC and APNS algorithms have evident improving than AP algorithm in time and space complexity. They can not only get a good clustering quality for massive data sets, but also filtrate noises and isolates well. So we can say they are two effective clustering algorithms with much applied prospect. At last, several research directions are presented.

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