Dynamic deformation monitoring of an offshore platform structure with accelerometers

In this paper, structural characteristics are evaluated by displacement and frequency indicators that indicate the real-time health status of offshore platforms. This paper uses an accelerometer to collect the dynamic response of the platform in the event of a ship collision. The main contributions of this research are reflected in three aspects. Firstly, based on Empirical Mode Decomposition (EMD) multiscale decomposition, the noise range is determined according to the scale and the average value of the standardized accumulation mode, and the original acceleration sequence is denoised. Secondly, two impact tests were carried out to understand the platform's structural characteristics under an external load. Combined with the FFT algorithm and Hilbert Huang transform, the three-dimensional information of the time, frequency, and energy is analyzed. Finally, a method of high-frequency dynamic displacement reconstruction is proposed. According to the extracted vibration frequency information, the parameters for the filter are reasonably set, and the denoised acceleration time sequence is processed with bandpass filtering and quadratic integration to obtain the high-frequency dynamic displacement of the structure. The results show that the high-frequency dynamic displacement of the accelerometer reconstruction is 1.5 mm. Two collision event frequencies, 1.477 Hz and 1.483 Hz, were successfully extracted from the north direction.

Prediction of H-Type Hypertension Based on Pulse Wave MFCC Features Using Mixed Attention Mechanism

H-type hypertension increases the risks of stroke and cardiovascular disease, posing a great threat to human health. Pulse diagnosis in traditional Chinese medicine ( TCM ) combined with deep learning can independently predict suspected H-type hypertension patients by analyzing their pulse physiological activities. However, the traditional time-domain feature extraction has a higher noise and baseline drift, affecting the classification accuracy. In this literature, we propose an effective prediction on frequency-domain pulse wave features. First, we filter time-domain pulse waves via removal of high-frequency noises and baseline shift. Second, Hilbert-Huang Transform is explored to transform time-domain pulse wave into frequency-domain waveform characterized by Mel-frequency cepstral coefficients (MFCC). Finally, an improved BiLSTM model, combined with mixed attention mechanism is built to applied for prediction of H-type hypertension. With 337 clinical cases from Longhua Hospital affiliated to Shanghai University of TCM and Hospital of Integrated Traditional Chinese and Western Medicine, the 3-fold cross-validation results show that sensitivity, specificity, accuracy, F1-score and AUC reaches 93.48%, 95.27%, 97.48%, 90.77% and 0.9676, respectively. The proposed model achieves better generalization performance than the classical traditional models. In addition, we calculate the feature importance both in time-domain and frequency-domain according to purity of nodes in Random Forest and study the correlations between features and classification that has a good reference value for TCM clinical auxiliary diagnosis.

Understanding the nonlinear behavior of EEG with advanced machine learning in artifact elimination

Steady-state Visually Evoked Potential (SSVEP) based Electroencephalogram (EEG) signal is utilized in brain-computer interface paradigms, diagnosis of brain diseases, and measurement of the cognitive status of the human brain. However, various artifacts such as the Electrocardiogram (ECG), Electrooculogram (EOG), and Electromyogram (EMG) are present in the raw EEG signal, which adversely affect the EEG-based appliances. In this research, Adaptive Neuro-fuzzy Interface Systems (ANFIS) and Hilbert-Huang Transform (HHT) are primarily employed to remove the artifacts from EEG signals. This work proposes Adaptive Noise Cancellation (ANC) and ANFIS based methods for canceling EEG artifacts. A mathematical model of EEG with the aforementioned artifacts is determined to accomplish the research goal, and then those artifacts are eliminated based on their mathematical characteristics. ANC, ANFIS, and HHT algorithms are simulated on the MATLAB platform, and their performances are also justified by various error estimation criteria using hardware implementation.

Defect type identification of thin-walled stainless steel seamless pipe based on eddy current testing

Aimed at eddy current detection of defects in thin-walled stainless steel seamless pipes, an effective detection method for identifying defect types is proposed. First, the empirical mode decomposition (EMD) method is used to process the collected eddy current signals and obtain the principal intrinsic mode function (IMF) components of different defects. The Hilbert-Huang transform (HHT) is used to extract the frequency-domain features of the principal IMF components, which are combined with the time-domain features to form an effective defect feature vector. Then, principal component analysis (PCA) is used to reduce the dimensions of the defect feature vector and the redundant information is removed to obtain the principal component vector of the defect. Finally, two radial basis function (RBF) neural networks are used to identify and classify the defect types and three error evaluation indicators are selected to evaluate the performance of the classification network models.