Detection of combustible gases is very important to reduce the modality and disability of human in both of civil and military situation.In this paper, a method of detection combustible gases of acetone and ethanol was proposed by using back propagation neural network (BPNN) and principal component analysis (PCA).The gas data were collected using some metal oxide semiconductor (MOS) gas sensors exposed to the mixture combustible gases of different concentration.The features of low and high frequency domain were extracted to establish a feature vector of 432 dimensions.Then PCA was used to reduce the dimension of feature vector from 432 to 11 which retained 99% information.The results showed the binary classification accuracy of BPNN is up to 100% for train, validation and test when distinguishing the combustible gas from the air.The mean and variance of error (0.004±0.008) for concentration prediction were obtained based on BPNN and PCA.The results demonstrated that the proposed method is effective for classification and concentration prediction of combustible gas.
Remote Photoplethysmography (rPPG) is a non-contact technique for measuring physiological signals using facial videos, exhibiting broad application prospects in fields such as anti-spoofing face recognition, healthcare, and affective computing. However, extracting rPPG signals from facial video sequences encounters challenges due to subtle color variations and noise interference. Additionally, the presence of phase offset between ground truth and facial videos further complicates this endeavor. To address the issues of weak signals, strong noise, and phase offset, weproposes a self-similarity supervised learning approach, named SimFuPulse, to mitigate noise and enhance rPPG representation by fusing original and differential video frames. By employing a 3D convolutional network (ResPhys) with an encoder-decoder architecture, enhanced spatiotemporal features are modeled to extract reliable rPPG signals. Moreover, a self-similarity mechanism is devised to mitigate the impact of phase offset on model training. The proposed method demonstrates superior accuracy over current state-of-the-art approaches across three publicly available datasets.
Since the release of ChatGPT and GPT-4, large language models (LLMs) and multimodal large language models (MLLMs) have garnered significant attention due to their powerful and general capabilities in understanding, reasoning, and generation, thereby offering new paradigms for the integration of artificial intelligence with medicine. This survey comprehensively overviews the development background and principles of LLMs and MLLMs, as well as explores their application scenarios, challenges, and future directions in medicine. Specifically, this survey begins by focusing on the paradigm shift, tracing the evolution from traditional models to LLMs and MLLMs, summarizing the model structures to provide detailed foundational knowledge. Subsequently, the survey details the entire process from constructing and evaluating to using LLMs and MLLMs with a clear logic. Following this, to emphasize the significant value of LLMs and MLLMs in healthcare, we survey and summarize 6 promising applications in healthcare. Finally, the survey discusses the challenges faced by medical LLMs and MLLMs and proposes a feasible approach and direction for the subsequent integration of artificial intelligence with medicine. Thus, this survey aims to provide researchers with a valuable and comprehensive reference guide from the perspectives of the background, principles, and clinical applications of LLMs and MLLMs.
The central arterial pressure (CAP) serves as a crucial parameter for assessing cardiovascular health and evaluating the risk of related diseases. Its non-invasive, continuous, and accurate reconstruction is crucial for the evaluation and prevention of cardiovascular diseases. However, traditional approaches often exhibit limited accuracy, while certain deep learning models face challenges in feature extraction, which limits their widespread use and clinical adoption. This study introduces a novel waveform reconstruction model (CBL-iTransformer), which is built upon an enhanced iTransformer architecture. It integrates bidirectional long short-term memory networks (BiLSTM) and convolutional neural networks (CNN) to augment the feature extraction efficiency and precision during the waveform reconstruction process. The performance of the CBL-iTransformer model in reconstructing the CAP waveform is discussed and validated using radial arterial pressure (RAP) waveform and CAP waveform data obtained from 62 patients who underwent invasive measurements before and after medication. The evaluation metrics, including the mean absolute error (MAE) and root mean square error (RMSE), are compared against tranditional method as well as a range of deep learning models. The research findings demonstrate that the model achieves a robust reconstruction of the CAP waveform ( : 0.93 ± 0.90 mmHg , : 1.29 ± 0.90 mmHg), while also yielding reliable reconstruction results for the central aortic systolic pressure (CASP) and central aortic diastolic pressure (CADP) waveforms ( : 1.44 ± 0.84 mmHg , : 1.30 ± 0.78 mmHg). The CBL-iTransformer model demonstrates excellent CAP reconstruction performance and is expected to be applied to clinical practice in the future.
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