An Intelligent Online Drunk Driving Detection System Based on Multi-Sensor Fusion Technology

Liu, Juan; Luo, Yuan; Ge, Liang; Zeng, Wen; Rao, Ziyang; Xiao, Xiaoting

doi:10.3390/s22218460

Cited by 4 publications

(4 citation statements)

References 19 publications

(19 reference statements)

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“…In Kalman filter, Q is used in Eq (12). Therefore, in the new adaptive scheme, Eq (12) is replaced by an online search for the prior error covariance of the posterior sequence through feedback.…”

Section: Cakf Modelmentioning

confidence: 99%

“…As shown in Fig 16) and ( 17) with (12) of the Kalman filter, the characteristics of CAKF scheme are as follows:…”

Section: Cakf Modelmentioning

confidence: 99%

“…When using single sensor for positioning of permanent magnet maglev trains, the positioning accuracy is degraded due to the presence of noise and occlusion in the environment, therefore a multi-sensor information fusion method is employed to solve this problem. Multi-sensor fusion technology applies data fusion [ 8 , 9 ] to target tracking [ 10 ], vehicle localization [ 11 ] and other fields [ 12 – 14 ], which solves some problems of low accuracy in many cases and has broad application prospects and great scientific value [ 15 ]. In order to meet the requirements for positioning of permanent magnet magnetic levitation trains, multiple sensors are generally installed on the maglev trains for data acquisition.…”

Section: Introductionmentioning

confidence: 99%

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Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm

Xu,

Fan,

et al. 2023

PLoS ONE

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Since the positioning accuracy of sensors degrades due to noise and environmental interference when a single sensor is used to localize a suspended rare-earth permanent magnetically levitated train, a multi-sensor information fusion method using multiple sensors and self-correcting weighting is proposed for permanent magnetic levitated train localization. A decay memory factor is introduced to reduce the weight of the influence of historical measurement data on the fusion estimation, thus enhancing the robustness of the fusion algorithm. The Kalman filtering results suffer from inaccuracy when process noise is present in the system. In this paper, we use a covariance adaptive scheme that replaces the prediction step of the Kalman filter with covariance. It uses the covariance adaptive scheme to search the posterior sequence online and reconstruct the prior error covariance. Since the process noise covariance is not used in the new adaptive scheme, the negative impact of the mismatch noise statistics is greatly reduced. Simulation and experimental results show that the use of multi-sensor information fusion and covariance adaptive Kalman algorithm has significant advantages in terms of adaptability, accuracy and simplicity.

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Section: Cakf Modelmentioning

confidence: 99%

“…As shown in Fig 16) and ( 17) with (12) of the Kalman filter, the characteristics of CAKF scheme are as follows:…”

Section: Cakf Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm

Xu,

Fan,

et al. 2023

PLoS ONE

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“…At the same time, terrorism, explosions, and toxic gas leakages have seriously endangered lives and caused property damage as well. [ 208,209 ] The gas sensors worn on security personnel can find mobile threats immediately by monitoring the toxic or flammable gases, thereby preventing the safety accidents. The gas sensors worn on drivers can monitor their alcohol concentration in real‐time and send the information to the traffic management department through the internet to prevent the occurrence of drunk driving accidents in time.…”

Section: The Applications Of Wearable Gas Sensorsmentioning

confidence: 99%

Room Temperature Wearable Gas Sensors for Fabrication and Applications

Xiao

Wang

et al. 2023

Advanced Sensor Research

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The recent surge in demand for human–machine interaction (HMI), Internet of Things (IoTs), and artificial intelligence (AI) has created both opportunities and challenges for room‐temperature wearable gas sensors. These sensors serve as a source of perceptual information and can be easily integrated into wearable electronic devices due to their portability and miniaturization. In recent years, various types of wearable room temperature gas sensors have been developed for fields like environmental monitoring, healthcare, smart home, industrial security, food safety monitoring, and public security. These sensors not only adjust to the movements of human effortlessly but also have reduced power consumption. Therefore, room temperature wearable gas sensors hold great promise for the development of integrated intelligent gas sensing system worn on the human body. These sensors can be fabricated using various sensing materials to detect diverse target gases. This review provides a comprehensive summary of the preparation of sensing materials with extraordinary sensing capabilities at room temperature. Additionally, the article includes a brief discussion of the sensing mechanism, employing four models to explain it: oxygen adsorption, direct electron transfer, proton transfer, and ions conduction. Finally, this article discusses the various applications and future perspectives of room‐temperature wearable gas sensors.

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A deep learning approach for gas sensor data regression: Incorporating surface state model and GRU-based model

Zhuang,

Yin,

et al. 2024

APL Machine Learning

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Metal–oxide–semiconductor (MOS) gas sensors are widely used for gas detection and monitoring. However, MOS gas sensors have always suffered from instability in the link between gas sensor data and the measured gas concentration. In this paper, we propose a novel deep learning approach that combines the surface state model and a Gated Recurrent Unit (GRU)-based regression to enhance the analysis of gas sensor data. The surface state model provides valuable insights into the microscopic surface processes underlying the conductivity response to pulse heating, while the GRU model effectively captures the temporal dependencies present in time-series data. The experimental results demonstrate that the theory guided model GRU+β outperforms the elementary GRU algorithm in terms of accuracy and astringent speed. The incorporation of the surface state model and the parameter rate enhances the model’s accuracy and provides valuable information for learning pulse-heated regression tasks with better generalization. This research exhibits superiority of integrating domain knowledge and deep learning techniques in the field of gas sensor data analysis. The proposed approach offers a practical framework for improving the understanding and prediction of gas concentrations, facilitating better decision-making in various practical applications.

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An Intelligent Online Drunk Driving Detection System Based on Multi-Sensor Fusion Technology

Cited by 4 publications

References 19 publications

Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm

Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm

Room Temperature Wearable Gas Sensors for Fabrication and Applications

A deep learning approach for gas sensor data regression: Incorporating surface state model and GRU-based model

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