Real-time driver drowsiness estimation by multi-source information fusion with Dempster–Shafer theory

Li, Xuanpeng; Emmanuel, Sabu; Lambert, Alain; Loonis, Pierre

doi:10.1177/0142331213508804

Cited by 11 publications

(6 citation statements)

References 29 publications

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“…It is unacceptable to use sensors. We decide from more accurate and accurate sources of information as shown in Figure 1: [10,11]. The specific form of the method is as follows:…”

Section: Information Fusion and Optimization Ofmentioning

confidence: 99%

[Retracted] College English Teaching Quality Evaluation System Based on Information Fusion and Optimized RBF Neural Network Decision Algorithm

Chen

2021

Journal of Sensors

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In the process of deepening and developing the current higher education reform, people pay more and more attention to the research of college English education. The key to improve the college English education is to improve the quality of education, and learning evaluation is the key measure to improve the quality of education and training. This paper mainly studies the college English teaching quality evaluation system based on information fusion and optimized RBF neural network decision algorithm. This paper analyzes the main problems and complexity of creating an ideal learning quality evaluation system. On the basis of analyzing the advantages and disadvantages of the previous learning quality evaluation methods, this paper summarizes the existing learning quality evaluation methods and puts forward some suggestions according to the existing evaluation methods. A learning quality evaluation model based on RBF algorithm of neural network is proposed. RBF regularization network method, RBF neural network decision algorithm, and experimental investigation method are used to study the college English teaching quality evaluation system based on information fusion and optimization of RBF neural network decision algorithm. By innovating teaching methods and enriching teaching means, college students’ thirst for English knowledge can be aroused, and teachers’ teaching level can be improved. The results show that 50% of college students think that the level of college English teaching is average and needs to be improved. In the performance evaluation system of college English teaching quality based on information fusion and optimized RBF neural network decision algorithm, it is necessary to establish a learning evaluation system, monitor the learning quality in real time, find problems and improve them in time, and recognize the current situation of education.

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“…It is unacceptable to use sensors. We decide from more accurate and accurate sources of information as shown in Figure 1: [10,11]. The specific form of the method is as follows:…”

Section: Information Fusion and Optimization Ofmentioning

confidence: 99%

[Retracted] College English Teaching Quality Evaluation System Based on Information Fusion and Optimized RBF Neural Network Decision Algorithm

Chen

2021

Journal of Sensors

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“…DST is an effective tool to handle uncertainty for a variety of real-world applications. Knowledge reasoning and decision making are typical examples of DST applications in practice (Deng, 2015; Li et al, 2014). Evidence theory was first proposed by Dempster and then further developed by Shafer.…”

Section: Related Work and Motivationsmentioning

confidence: 99%

Adaptive and robust evidence theory with applications in prediction of floor water inrush in coal mine

Zhang

Chen

et al. 2017

Transactions of the Institute of Measurement and Control

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The Internet of Things generates rich information either from different sources or the same source via different measurement methods. This demands data fusion for decision making. Despite the progress in data fusion, existing data fusion techniques, such as the classic Dempster–Shafer evidence Theory, face challenges when dealing with highly conflicting sources of evidence. To address this problem, an Adaptive and Robust evidence Theory (ART) is presented in this paper through a robust combination of conjunctive and disjunctive rules. It is capable of handling both conflicting and reliable sources of evidence. When the sources of evidence are reliable, the conjunctive rule plays a predominant role, whereas if the sources of evidence are in high conflict the disjunctive rule is critical. Our ART approach was compared with existing representative evidence theory methods through two examples, and was further applied in the prediction of floor water inrush in coal mines. The ART approach presented in this paper was demonstrated to behave better than the existing methods.

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“…However, the information fusion from the same source for the reconstitution of an environment, a state, or multiple sources for decisionmaking has been supported by researchers from different areas in recent years. In the field of road safety, [8] has fused information using the theory of Dempster-Shafer to improve the temporal response of the drivers monitoring in real-time. In another contribution, the support Vector Machine (SVM) method has been used for the data fusion of a diesel engine fault diagnosis [9].…”

Section: Introductionmentioning

confidence: 99%

Multi Sources Information Fusion Based on Bayesian Network Method to Improve the Fault Prediction of Centrifugal Compressor

Nessaib

Lakehal

2022

Strojnícky Časopis - Journal of Mechanical Engineering

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The centrifugal compressor is an important machine in the oil and gas industry, so the fault prediction of these machines is widely discussed in the literature. Several techniques can and should be used in fault prediction of centrifugal compressors: vibration analysis, non-destructive testing techniques, operating parameters, and other techniques. But in particular cases, these tools are inefficient for making a decision regarding the combined fault diagnosis and prediction. This paper presents a contribution to fault prediction in centrifugal compressor utilizing multi-source information fusion by a Bayesian network. The data fusion does not come from the same source, but rather from vibration analysis, oil analysis, and operating parameters. In addition, the accuracy and ability of fault prediction can be improved compared with the use of data obtained from vibration analysis only or oil analysis. The proposed method accuracy is validated on a BCL 406 type centrifugal compressor. Furthermore, the obtained results showed the effectiveness of the multi-source information fusion by Bayesian network approach gives more accuracy to decision-making in fault prediction and the developed method has an effect in predicting the combined faults.

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Real-time driver drowsiness estimation by multi-source information fusion with Dempster–Shafer theory

Cited by 11 publications

References 29 publications

[Retracted] College English Teaching Quality Evaluation System Based on Information Fusion and Optimized RBF Neural Network Decision Algorithm

[Retracted] College English Teaching Quality Evaluation System Based on Information Fusion and Optimized RBF Neural Network Decision Algorithm

Adaptive and robust evidence theory with applications in prediction of floor water inrush in coal mine

Multi Sources Information Fusion Based on Bayesian Network Method to Improve the Fault Prediction of Centrifugal Compressor

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