Indoor location service in support of a smart manufacturing facility

Carrasco, Uises; Coronado, Pedro Daniel Urbina; Parto, Mahmoud

doi:10.1016/j.compind.2018.09.009

Cited by 22 publications

(16 citation statements)

References 16 publications

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“…With the rapid development of computers, computing resources no longer have been an obstacle to the development of machine learning. Thus, using machine learning algorithms to solve indoor positioning problem has become a research hotspot [24], [25], [31]. The KNN localization algorithm based on the RSSI has been divided mainly into two stages, including an offline and online stage [32].…”

Section: Hybrid Knn Algorithm Based On the Rssimentioning

confidence: 99%

“…To verify whether this method of a fusion KNN algorithm has certain adaptability, this work also proposes a new hybrid weighted Bayesian algorithm (defined as H-WBayesian) based on the self-adaptive dynamic ranging model, maximum likelihood estimation algorithm and Bayesian algorithm. The traditional Bayesian algorithm is a typical probabilistic technique, which has been widely applied in the field of indoor positioning [24], [31], [35], [36]. The principle of the traditional Bayesian algorithm is to preset a certain number of position nodes and give the prior probability distribution of these position nodes and the conditional probability of the position feature.…”

Section: Hybrid Bayesian Algorithm Based On the Rssimentioning

confidence: 99%

“…The position with the largest posterior probability is the result of position estimation. An RSSI-based Bayesian positioning algorithm has been divided into offline and online stages [24], [31]. The main task of the offline phase is to obtain the location information of each key node and the RSSI characteristics at the corresponding location to build a location fingerprint database.…”

Section: Hybrid Bayesian Algorithm Based On the Rssimentioning

confidence: 99%

“…where ( | ) represents the probability of the mobile terminal appearing at the position under the condition of obtaining the new observation value , which is called the posterior probability; ( ) represents the probability distribution of the observation value. Since this value is independent of the position , it can be regarded as a constant when only the relative probability of the posterior probability is required to be solved [24], [31]; ( ) denotes the probability of appearing at position ; namely, the prior probability. ( | ) is called the conditional probability or the likelihood function, which represents the observation probability of different access points at the specified position.…”

Section: Hybrid Bayesian Algorithm Based On the Rssimentioning

confidence: 99%

See 3 more Smart Citations

Self-Adaptive Dynamic Ranging Model-Based Real-Time Hybrid Algorithm for Accurate Indoor Localization

Jiang¹,

Huang²,

Xiao³

et al. 2020

IJCCE

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The reliability of location information still maintains a crucial impact on restricting the development of location based services in indoor environment. However, in wireless local area network, received signal strength indicator (RSSI) is prone to be interfered by indoor complex environment, resulting in low accuracy and instability of real-time positioning. Here, the new self-adaptive dynamic ranging model-based real-time hybrid algorithm was proposed to realize accurate and undisturbed localization in indoor scenes. A self-adaptive dynamic ranging model was initially constructed to update the environmental parameters and correct the ranging values of mobile terminals in real time. Based on this model, a hybrid KNN algorithm and a hybrid Bayesian algorithm were severally presented. Location fingerprint database and real-time RSSI data of test points were then obtained through data acquisition. Finally, the acquired data was further used to verify the two hybrid algorithms proposed, and compared with the results of several conventional algorithms. As a result, the stability and accuracy of dual hybrid algorithms were better than those of the traditional ones. The range of average location error of both hybrid algorithms maintained 1.26-1.38 m, which was significantly lower than the error level of 2-5 m under the current WLAN environment. This newly proposed hybrid algorithm could effectively improve the stability and accuracy of indoor localization with real-time positioning algorithm, providing a promising solution for RSSI-based indoor positioning system.

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Section: Hybrid Knn Algorithm Based On the Rssimentioning

confidence: 99%

Section: Hybrid Bayesian Algorithm Based On the Rssimentioning

confidence: 99%

Section: Hybrid Bayesian Algorithm Based On the Rssimentioning

confidence: 99%

Section: Hybrid Bayesian Algorithm Based On the Rssimentioning

confidence: 99%

See 2 more Smart Citations

Self-Adaptive Dynamic Ranging Model-Based Real-Time Hybrid Algorithm for Accurate Indoor Localization

Jiang¹,

Huang²,

Xiao³

et al. 2020

IJCCE

View full text Add to dashboard Cite

show abstract

“…[3,5,6,8,9]. Such objectives can be achieved by collecting digital data from the shopfloor in real-time, decision making, and systematic operations management based on the collected data [10][11][12][13]. Data analysis techniques, such as artificial intelligence (AI) and data mining, can be used to extract meaningful knowledge and patterns from the collected data.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Rework Process Management System under Smart Factory Environment

Kim

2020

Sustainability

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Rework for defective items is very common in practical shopfloors; however, it generally causes unnecessary energy consumptions and operational costs. In order to address this problem, we propose a novel approach called the intelligent rework process management (i-RPM) system. The proposed system is based on intelligent rework policy, which provides a preventive rework procedure for items with latent defects. Such items can be detected before quality tests by applying conventional classification techniques. Moreover, training sets for the classification algorithms can be collected by using modern information and communications technology (ICT) infrastructures. Items with latent defects are not allowed to proceed to the following processes under intelligent rework policy. Instead, they are returned to the preceding processes for rework in order to avoid unnecessary losses on the shopfloor. Consequently, the proposed system helps to achieve a sustainable manufacturing system. Nevertheless, misclassification by the classification model can degrade the performance of intelligent rework policy. Therefore, the i-RPM system is designed to compare rework policies based on classification accuracy and choose the best one of them. For illustration, we applied the i-RPM system to the rework procedure of a steel manufacturer located in Busan, South Korea, and our experiment results revealed that the cost reduction effect of the intelligent rework policy is affected by several input parameters.

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Nutzung App-basierter Orientierungshilfen im stationären Einzelhandel

Wohllebe,

Johnsen

2023

FOM-Edition

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Indoor location service in support of a smart manufacturing facility

Cited by 22 publications

References 16 publications

Self-Adaptive Dynamic Ranging Model-Based Real-Time Hybrid Algorithm for Accurate Indoor Localization

Self-Adaptive Dynamic Ranging Model-Based Real-Time Hybrid Algorithm for Accurate Indoor Localization

Intelligent Rework Process Management System under Smart Factory Environment

Nutzung App-basierter Orientierungshilfen im stationären Einzelhandel

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