A neural network approach for safety monitoring applications

Marinković, Zlatica; Atanasković, Aleksandar; Xibilia, Maria Gabriella; Pace, Calogero; Latino, M.; Donato, Nicola

doi:10.1109/sas.2016.7479862

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…At the same time, sensors may be affected by the complicated application environment. Thus, multi-sensor modeling and sensor data fusion are important issues in many real applications [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Driven by real applications, many methods have been proposed for multi-sensor modeling and sensor data fusion [ 8 ], including neural network models [ 1 , 9 ], belief function theory [ 10 , 11 ], Dempster–Shafer evidence theory [ 12 , 13 , 14 ], fuzzy set theory [ 15 ], Z-Numbers [ 16 ], and so on [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion

Tang

Zhou

et al. 2017

Sensors

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In real applications, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. In this paper, in the frame of Dempster–Shafer evidence theory, a weighted belief entropy based on Deng entropy is proposed to quantify the uncertainty of uncertain information. The weight of the proposed belief entropy is based on the relative scale of a proposition with regard to the frame of discernment (FOD). Compared with some other uncertainty measures in Dempster–Shafer framework, the new measure focuses on the uncertain information represented by not only the mass function, but also the scale of the FOD, which means less information loss in information processing. After that, a new multi-sensor data fusion approach based on the weighted belief entropy is proposed. The rationality and superiority of the new multi-sensor data fusion method is verified according to an experiment on artificial data and an application on fault diagnosis of a motor rotor.

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Section: Introductionmentioning

confidence: 99%

A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion

Tang

Zhou

et al. 2017

Sensors

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“…The real-time estimation obtained by the SS can be used by a controller, while the corresponding delayed measurements allow the model to be improved, by avoiding an error propagation effect. Besides their use for plant control, they are used to approach a number of other different problems as well, such as measuring system back-up, what-if analysis, sensor validation and fault diagnosis [7,[30][31][32][33][34][35][36][37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Input Selection Methods for Soft Sensor Design: A Survey

2020

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Soft Sensors (SSs) are inferential models used in many industrial fields. They allow for real-time estimation of hard-to-measure variables as a function of available data obtained from online sensors. SSs are generally built using industries historical databases through data-driven approaches. A critical issue in SS design concerns the selection of input variables, among those available in a candidate dataset. In the case of industrial processes, candidate inputs can reach great numbers, making the design computationally demanding and leading to poorly performing models. An input selection procedure is then necessary. Most used input selection approaches for SS design are addressed in this work and classified with their benefits and drawbacks to guide the designer through this step.

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“…The neural network approach to safety monitoring applications was investigated by Marinkovic et al A single artificial neural network was used to determine gas concentrations based on sensor array measurements, which worked at the same time as temperature compensation and the effect of humidity on the sensor output [19]. Rossi and Brunelli observed gas sensing in an unmanned vehicle comprising a UAV Quadrotor and a gas sensor [20].…”

Section: Introductionmentioning

confidence: 99%

Gas Pressure Measurement Device and Medical Vacuum Design

Tunggal

Sanjaya

Widodo

et al. 2020

jrc

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Two methods are used in a digital pressure meter available in the market, namely positive pressure, and negative pressure. The positive one is used to measure the air pressure on a sphygmomanometer and medical gas pressure at an outlet in the treatment room so that operators can easily check medical gas pressure for the safety of the patient, or they can routinely check to find out how much pressure of the medical gas. Based on the background, the research aimed to design a digital pressure meter equipped with a medical gas measurement mode so that the device can be used to calibrate the sphygmomanometer, suction pump, and measure the medical gas pressure available at the medical gas outlet in each treatment room.

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A neural network approach for safety monitoring applications

Cited by 8 publications

References 16 publications

A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion

A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion

Input Selection Methods for Soft Sensor Design: A Survey

Gas Pressure Measurement Device and Medical Vacuum Design

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