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Number of results: 5
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Non-invasive Ultrasound Doppler Effect Based Method of Liquid Flow Velocity Estimation in Pipe

Pawel Biernacki Stanislaw Gmyrek Wladyslaw Magiera
Archives of Acoustics | 2023 | vol. 48 | No 1 | 141-148 | DOI: 10.24425/aoa.2023.144268
Keywords information fusion flow velocity Doppler effect
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Abstract

This paper discusses the estimation of flow velocity from a multi-sensor scenario. Different estimation methods were used, which allow the effective measurement of the actual Doppler shift in a noisy environment, such as water with air bubbles, and on this basis the estimation of the flow velocity in the pipe was calculated. Information fusion is proposed for the estimates collected. The proposed approach focuses on the density of the fluid. The proposed method is capable of determining the flow velocity with high accuracy and small variations. Simulation results for plastic and steel (both galvanized and non-galvanized) pipes show the possibility of accurate fluid flow measurements without the need for sensors inside the pipe.
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Authors and Affiliations

Pawel Biernacki
1
Stanislaw Gmyrek
1
Wladyslaw Magiera
1
  1. Faculty of Electronics, Photonics and Microsystems, Department of Acoustics, Multimedia and Signal Processing, Wroclaw University of Science and Technology, Wrocław, Poland

Information fusion method of multichannel nanosensors based on neural network

Chaoke Li
Bulletin of the Polish Academy of Sciences Technical Sciences | 2022 | 70 | 2 | e140258 | DOI: 10.24425/bpasts.2022.140258
Keywords nanosensors multiplexing information fusion data fusion neural network
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Abstract

Information fusion approaches have been commonly used in multi sensor environments for the fusion and grouping of data from various sensors which is used further to draw a meaningful interpretation of the data. Traditional information fusion methods have limitations such as high time complexity of fusion processes and poor recall rate. In this work, a new multi-channel nano sensor information fusion method based on a neural network has been designed. By analyzing the principles of information fusion methods, the back propagation based neural network (BP-NN) is devised in this work. Based on the design of the relevant algorithm flow, information is collected, processed, and normalized. Then the algorithm is trained, and output is generated to achieve the fusion of information based on multi-channel nano sensor. Moreover, an error function is utilized to reduce the fusion error. The results of the present study show that compared with the conventional methods, the proposed method has quicker fusion (integration of relevant data) and has a higher recall rate. The results indicate that this method has higher efficiency and reliability. The proposed method can be applied in many applications to integrate the data for further analysis and interpretations.
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Authors and Affiliations

Chaoke Li
1
  1. School of Intelligent Medical Engineering, Sanquan College of Xinxiang Medical University, Xinxiang 453003, China

Hardness identification of rock based on multi-sensor information fusion during the process of roadway tunnelling

H. Wang S. Lu M. Huang X. Zhao
Bulletin of the Polish Academy of Sciences Technical Sciences | 2020 | 68 | No. 6 | 1301-1309 | DOI: 10.24425/bpasts.2020.135381
Keywords Rock hardness identification membership function information fusion Dempster-Shafer
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Abstract

In this paper, a new dynamic model was proposed for identifying the rock hardness during the process of roadway tunnelling, thereby regulating the speed of the driving motor and the torque of the cutting head. The presented identification model establishes a multi-information feature database containing vibration signals in the y-axis, acoustic emission signals, cutting current signals, and temperature signals. Subsequently, we obtain the membership functions (MFs) of the given multiple signals with the amount of feature samples according to the principle of minimum fuzzy entropy. Furthermore, a rock hardness identification model was established based on multi-sensor information fusion and Dempster-Shafer (D-S) evidence theory. To prove the accuracy of the proposed model, an identification experiment was carried out through the cutting of a poured mixed rock specimen with five grades of hardness. As a result, the proposed identification model recognizes the rock hardness accurately for fifteen sampling points, which indicates the significance of the method with regard to the dynamic identification of rock hardness during the process of roadway tunnelling, and further provides data support for adjusting the speed of the cutting head adaptively, thereby achieving high efficiency tunnelling.

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Authors and Affiliations

H. Wang
ORCID: ORCID
S. Lu
M. Huang
X. Zhao

Estimation of vehicle sideslip angle via pseudo-multisensor information fusion method

Te Chen Long Chen Yingfeng Cai Xing Xu
Metrology and Measurement Systems | 2018 | vol. 25 | No 3 | 499-516 | DOI: 10.24425/123902
Keywords vehicle state estimation sideslip angle recursive least squares multi-sensor information fusion pseudo-measurements
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Abstract

This paper presents a novel sideslip angle estimator based on the pseudo-multi-sensor fusion method. The

kinematics-based and dynamics-based sideslip angle estimators are designed for sideslip angle estimation.

Also, considering the influence of ill-conditioned matrix and model uncertainty, a novel sideslip angle estimator

is proposed based on the wheel speed coupling relationship using a modified recursive least squares

algorithm. In order to integrate the advantages of above three sideslip angle estimators, drawing lessons

from the multisensory information fusion technology, a novel thinking of sideslip angle estimator design is

presented through information fusion of pseudo-multi-sensors. Simulations and experiments were carried

out, and effectiveness of the proposed estimation method was verified.

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Authors and Affiliations

Te Chen
Long Chen
Yingfeng Cai
Xing Xu

Grid Fault Diagnosis Based on Information Entropy and Multi-source Information Fusion

Xin Zeng Xingzhong Xiong Zhongqiang Luo
International Journal of Electronics and Telecommunications | 2021 | vol. 67 | No 2 | 143-148 | DOI: 10.24425/ijet.2021.135956
Keywords Information entropy Bayesian network Multisource information fusion D-S evidence theory fault diagnosis
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Abstract

In order to solve the problem of misjudgment caused by the traditional power grid fault diagnosis methods, a new fusion diagnosis method is proposed based on the theory of multisource information fusion. In this method, the fault degree of the power element is deduced by using the Bayesian network. Then, the time-domain singular spectrum entropy, frequencydomain power spectrum entropy and wavelet packet energy spectrum entropy of the electrical signals of each circuit after the failure are extracted, and these three characteristic quantities are taken as the fault support degree of the power components. Finally, the four fault degrees are normalized and classified as four evidence bodies in the D-S evidence theory for multifeature fusion, which reduces the uncertainty brought by a single feature body. Simulation results show that the proposed method can obtain more reliable diagnosis results compared with the traditional methods.
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Bibliography

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Authors and Affiliations

Xin Zeng
1 2
Xingzhong Xiong
1 3
Zhongqiang Luo
1 3
  1. School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, China
  2. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, China
  3. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan Universityof Science and Engineering, Yibin, China

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