In order to meet the demand for underground air quality monitoring by the mine frequency conversion ventilation system, a mine air quality evaluation method based on improved random forest is proposed, aiming at accurately monitoring the air quality inside the mine and providing an important reference for the frequency conversion ventilation system, so as to effectively safeguard the safe production and the miners' health. Firstly, analyze the sources of pollutants in the air of mines and their potential hazards to the health of miners, and the five main pollutants, namely carbon monoxide (CO), sulfur dioxide (SO2), hydrogen sulfide (H2S), nitrogen dioxide (NO2) and dust, are selected as the evaluation factors. Secondly, the standard for evaluating underground air quality was established, based on which an air quality evaluation rating system was set up and the corresponding data set was constructed accordingly. In this study, an improved random forest algorithm using AUC values is used to carry out a comprehensive evaluation of mine air quality. The experimental results show that the improved algorithm performs better than the original algorithm, with a minimum generalization error of only 0.0177 and a maximum classification accuracy of 97.72% for the test data. The method can better achieve the evaluation of air quality under the mine, with high robustness and stability. It provides new ideas and methods for the construction of smart mines and the evaluation of air quality in mines.
Many studies have shown that wireless sensing would be a promising method for liquid identification, but existing methods still have limitations for fine-grained liquid sensing. In this paper, we propose a liquid identification method based on multiple transceiver pairs, which effectively improves the sensing resolution of liquid identification. We implement our method with a commercially FMCW millimeter wave radar and evaluate its performance. Our result shows that for concentrations as low as 0.5% in alcohol solutions, our method can achieve an accuracy of more than 95%.
KEYWORDS: Signal detection, Ultrasonics, Defense and security, Signal attenuation, Accelerometers, Frequency response, Signal processing, Environmental sensing, Detection and tracking algorithms, Design and modelling
Inaudible attack has brought growing concerns over security of voice assistants. With a well-designed inaudible signal, an adversary can force the voice assistant to execute commands inaudibly like “Siri, open the door”. It is challenging to defend against ultrasonic attacks without modifying the hardware. In this paper, we proposed a light-weight system named IMUSHIELD to defend voice assistant against inaudible attack. By comparing the different response of signal from microphone and inertial measurement units (IMUs) to different frequencies on smartphones. IMUSHIELD is able to detect the attacks without modifying the hardware. We have prototyped our method on a number of smartphones and test the performance of IMUSHIELD comprehensively in the real world, the result shows that our average detection accuracy exceeded 90%.
To achieve non-invasive, non-contact, and real-time heart rate monitoring, proposed a pulse signal acquisition system using PVDF (Polyvinylidene Fluoride) piezoelectric film. In order to address the issue of errors in heart rate extraction caused by differences in the morphology of pulse signals across individuals or in different states, the K-means clustering algorithm was innovatively used to locate the peak of pulse waveforms in different states and constructed a heart rate data set. Real-time heart rate monitoring by training a large number of pulse signal samples with the proposed CNN-LSTM network model. Experimental results demonstrated that the performance metrics of this model, including the MAE (Mean Absolute Error), RMSE (Root Mean Square Error), and R2 (Coefficient of Determination), are 0.2517, 0.3395, and 0.9863, respectively. the maximum error between the proposed system and the standard instrument within three minutes was only 1.55 beats/minute, indicating that the system exhibits high accuracy and reliability, and holds great potential for applications in heart rate detection.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.