A Framework for Indoor Positioning Including Building Topology

Brunello, Andrea; Montanari, Angelo; Saccomanno, Nicola

doi:10.1109/access.2022.3218301

Cited by 9 publications

(8 citation statements)

References 35 publications

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“…[56]- [68] CMAPSS [69] Shapley Additive Explanations and Rule Based [35]- [37], [70]-[80] General Machine Faults and Failures [81] Feature Importance, Shapley Additive Explanation, Class Activation Mapping and Local Interpretable Modle-agnostic Explanation [42], [48], [82]- [87] Trains Feature Importance [34], [88]- [93] Gearboxes [94] Shapley Additive Explanations and Interpretable Filters [42], [45], [48], [95] Artificial Dataset Local Interpretable Modle-agnostic Explanation, Shapley Additive Explanation, Counterfactual and Surrogate [44], [96], [97] Hot [38], [70], [106] Lithium-ion Batteries [107] Layer-wise Relevance Propagation [37], [108], [109] Wind Turbines [110] Autoencoder-based Anomaly Root Cause Analysis and Sparse Networks [111], [112] Amusement Park Rides Depth-based Isolation Forrest Feature Importance and Accelerated Model-agnostic Explanations [113], [114] Particle Accelerators Layer-wise Relevance Propagation and Feature importance [115], [116] Chemical plant Shapley Additive Explanations [117], [118] Semi-conductors [119] Shapley Additive Explanations and Knowledgebased [120], [121] Aircraft Fuzzy…”

Section: Datasetsmentioning

confidence: 99%

Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities

Cummins,

Sommers,

Ramezani

et al. 2024

IEEE Access

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Predictive maintenance is a well studied collection of techniques that aims to prolong the life of a mechanical system by using artificial intelligence and machine learning to predict the optimal time to perform maintenance. The methods allow maintainers of systems and hardware to reduce financial and time costs of upkeep. As these methods are adopted for more serious and potentially life-threatening applications, the human operators need trust the predictive system. This attracts the field of Explainable AI (XAI) to introduce explainability and interpretability into the predictive system. XAI brings methods to the field of predictive maintenance that can amplify trust in the users while maintaining well-performing systems. This survey on explainable predictive maintenance (XPM) discusses and presents the current methods of XAI as applied to predictive maintenance while following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. We categorize the different XPM methods into groups that follow the XAI literature. Additionally, we include current challenges and a discussion on future research directions in XPM.

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Section: Datasetsmentioning

confidence: 99%

Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities

Cummins,

Sommers,

Ramezani

et al. 2024

IEEE Access

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“…Indoor positioning attracts more and more attention with the increasing requirement of positioning based on services recently [1][2][3]. In general, indoor positioning methods can be classified into three categories according to the parameter used: angle of the signal arrival (AOA) [4], time of the signal arrival (TOA) [5], and the received signal strength (RSS) or the received signal strength index (RSSI) [6].…”

Section: Introductionmentioning

confidence: 99%

A fast distributed indoor positioning based on received signal strength index

Liang¹,

Liu²,

Cai³

et al. 2023

International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022)

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The ubiquitous WiFi makes it a location service infrastructure and attracts more attention now. We use the cubic spline interpolation method to interpolate the attenuation factor n, and a wide range of indoor corridor environments test results show that the cubic spline interpolation method is more conducive to achieving the precise distance. We also improved the classical trilateration positioning algorithm with a priori knowledge of the specific positioning environment. The improved positioning algorithm significantly reduces positioning errors and improves positioning accuracy. The positioning method is transferred to the embedded positioning node to make distributed positioning with lower calculation cost.

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“…According to the definition, indoor navigation means detecting objects (devices or people) and orientation inside the building using radio waves, acoustic signals, wireless networks (Wi-Fi), infrared sensors, magnetic field, Bluetooth technology or other means [3,5].…”

Section: Introductionmentioning

confidence: 99%

“…According to recent studies, it makes the development of such a system expensive and long-term. It is also pointed out that it causes certain difficulties to its testing, maintenance and expansion opportunities [3,5,13,20].…”

Section: Introductionmentioning

confidence: 99%

Development of the information system for navigation in modern university campus

2023

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According to the aims, the paper highlights algorithmic, interface and technological solutions for the development of the information system for navigation in university campus. In the process of searching for algorithmic and interface solutions, the peculiarities of the scientific and applied problem of indoor navigation were analyzed, as well as the capabilities of selected analogues of navigation systems implementing similar functions of this subject domain were evaluated. It was concluded that the analyzed analogues, despite their significant functionality, have the set of limitations. The specification of functional and non-functional requirements for the university navigation system was carried out, its architecture was defined as a set of interconnected modules, for the implementation of which appropriate interface and algorithmic solutions were elaborated and covered. The main stages of design and development of the university navigation system in the context of the elaborated solutions are highlighted. The functionality of the implemented system is characterized. It was established that during the design it was possible to overcome the main limitations inherent in similar systems implementing indoor navigation. The results of the system implementation in the educational practice of a national university are highlighted. Feedback from users received during the approbation testified the feasibility of developing and using the information system for navigation in university campus. The prospects for further work are formulated.

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A Framework for Indoor Positioning Including Building Topology

Abstract: acknowledge the support of u-blox AG, for the projects Towards a uniform modeling framework for fingerprint-based positioning and Advanced solutions for indoor positioning, respectively.

Cited by 9 publications

References 35 publications

Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities

Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities

A fast distributed indoor positioning based on received signal strength index

Development of the information system for navigation in modern university campus

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