A Clustering-Based Approach for Improving the Accuracy of UWB Sensor-Based Indoor Positioning System

Arsan, Taner; Hameez, Mohammed Muwafaq Noori

doi:10.1155/2019/6372073

Cited by 12 publications

(7 citation statements)

References 31 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, the real‐time locating system (RTLS) based on ultra‐wideband (UWB) was the most adequate technology for the purpose of this research 49,51‐55 . Based on both the comparisons conducted by other authors 56 and our analysis and available resources, a Decawave ® 57 equipment was chosen as there were experimental precedents in several spaces and situations 58‐65 . This equipment is an ultra‐wideband network constituted by fixed nodes (anchors) and moving nodes (tags).…”

Section: Methodsmentioning

confidence: 99%

“…49,[51][52][53][54][55] Based on both the comparisons conducted by other authors 56 and our analysis and available resources, a Decawave ®57 equipment was chosen as there were experimental precedents in several spaces and situations. [58][59][60][61][62][63][64][65] This equipment is an ultra-wideband network constituted by fixed nodes (anchors) and moving nodes (tags). Regarding the accuracy, although manufacturers' technical requirements indicate ± 10 centimeters and second intervals, other indications were also considered.…”

Section: Detection Devicesmentioning

confidence: 99%

See 1 more Smart Citation

Distances of transmission risk of COVID‐19 inside dwellings and evaluation of the effectiveness of reciprocal proximity warning sounds

2020

View full text Add to dashboard Cite

One of the main modes of transmission and propagation of COVID‐19 (SARS‐CoV‐2) is the direct contact with respiratory droplets transmitted among individuals at a certain distance. There are indoor spaces, such as dwellings, in which the transmission risk is high. This research aims to record and analyze risk close contacts in this scope, experimentally assessing the effectiveness of using electronic proximity warning sound devices or systems. For this purpose, the methodology is based on monitoring the location of the occupants of a dwelling. Then, the days in which a proximity warning sound system is installed and activated are compared to the days in which the system is not activated. The results stressed the significant reduction of time and number of close contacts among individuals when the warning was activated. Regarding the relation between the number and the duration of close contacts, together with the reductions mentioned, the possibility of making certain predictions based on the distributions obtained is proved. All this contributes to the progress in the prevention of COVID‐19 transmission because of close contacts in dwellings.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Detection Devicesmentioning

confidence: 99%

Distances of transmission risk of COVID‐19 inside dwellings and evaluation of the effectiveness of reciprocal proximity warning sounds

2020

View full text Add to dashboard Cite

show abstract

“…The plot takes the shape of an arm, and the point at which the arm bends is used as the optimal k value. The silhouette coefficient is another method used to analyze the distance between the resulting clusters via visual representation to determine the best k-value, visually [9,10]. This method measures how similar a data point is to its own cluster (cohesion), compared to other clusters (separation).…”

Section: Related Workmentioning

confidence: 99%

A Robust Distributed Clustering of Large Data Sets on a Grid of Commodity Machines

Taamneh

Al-Hami

Bani-Salameh

et al. 2021

Data

View full text Add to dashboard Cite

Distributed clustering algorithms have proven to be effective in dramatically reducing execution time. However, distributed environments are characterized by a high rate of failure. Nodes can easily become unreachable. Furthermore, it is not guaranteed that messages are delivered to their destination. As a result, fault tolerance mechanisms are of paramount importance to achieve resiliency and guarantee continuous progress. In this paper, a fault-tolerant distributed k-means algorithm is proposed on a grid of commodity machines. Machines in such an environment are connected in a peer-to-peer fashion and managed by a gossip protocol with the actor model used as the concurrency model. The fact that no synchronization is needed makes it a good fit for parallel processing. Using the passive replication technique for the leader node and the active replication technique for the workers, the system exhibited robustness against failures. The results showed that the distributed k-means algorithm with no fault-tolerant mechanisms achieved up to a 34% improvement over the Hadoop-based k-means algorithm, while the robust one achieved up to a 12% improvement. The experiments also showed that the overhead, using such techniques, was negligible. Moreover, the results indicated that losing up to 10% of the messages had no real impact on the overall performance.

show abstract

“…(2) K-means clustering: K-means is considered a crucial clustering algorithm [25], which has an iterative process for datasets. In this iterative process, the datasets are divided into "k" predefined nonoverlapping clusters or subgroups.…”

Section: Cluster Analysismentioning

confidence: 99%

Analysis and Prediction of Overloaded Extra-Heavy Vehicles for Highway Safety Using Machine Learning

Lin

et al. 2020

Mobile Information Systems

View full text Add to dashboard Cite

Along with the prosperity and rapid development of the national economy, the transportation industry has rapidly developed in China. However, overloaded vehicles have been causing frequent traffic accidents. Thus, to alleviate or resolve the corresponding problems associated with highway engineering safety and the market economy, an improved technique for overload management is urgently required. In this study, to analyze the overload data on expressways and highways in China, we developed a machine learning model by comparing the performances of cluster analysis, backpropagation neural network (BPNN), generalized regression neural network (GRNN), and wavelet neural network (WNN) in analyzing global and local time series overload data. In a case study, our results revealed the trends of overloading on highways in Jiangsu Province. Given sufficient data, BPNN performed better than GRNN and WNN. As the amount of training data increased, GRNN performed better, but the runtime increased. WNN had the shortest runtime among the three methods and could reflect the future trends of the overload rate in the monthly data prediction of overload. Our model provides information with potential value for expressway network management departments through data mining. This information could help management departments allocate resources reasonably and optimize the information utilization rate.

show abstract

A Clustering-Based Approach for Improving the Accuracy of UWB Sensor-Based Indoor Positioning System

Cited by 12 publications

References 31 publications

Distances of transmission risk of COVID‐19 inside dwellings and evaluation of the effectiveness of reciprocal proximity warning sounds

Distances of transmission risk of COVID‐19 inside dwellings and evaluation of the effectiveness of reciprocal proximity warning sounds

A Robust Distributed Clustering of Large Data Sets on a Grid of Commodity Machines

Analysis and Prediction of Overloaded Extra-Heavy Vehicles for Highway Safety Using Machine Learning

Contact Info

Product

Resources

About