Design of Robust Heart Abnormality Detection System based on Wavelet Denoising Algorithm

The technology that continues to be developed by many researchers today is an automatic heart attack detection system based on an Electrocardiogram (ECG) signal. Several other studies have been carried out to build an Internet of Things (IoT) based heart abnormality detection system. Based on the analysis of related studies that have been carried out previously, several researchers have developed an ECG signal-based heart abnormality detection system using clean ECG signal data. While the reality of the concept of an IoT-based detection system, the process of recording ECG signal data on the sensor, the process of sending data to the server, and the process of retrieving data from the server are vulnerable to noise exposure. ECG signal containing noise will greatly affect the accuracy of system detection. This paper proposes the development of a noise-resistant heart condition detection system using a wavelet denoising algorithm. The process of denoising ECG signals using the Wavelet algorithm is generally able to improve the accuracy of detecting noisy ECG signals. The most significant increase in accuracy is seen in the low SNR value. The Daubechies 4 (db4) denoising algorithm is the best-performing algorithm. The ECG signal classification method uses the Artificial Neural Network (ANN) algorithm. Detection system hardware is also designed in this research using the concept based on the Internet of Things.

Research on Hyperspectral Inversion of Soil Alkaline Hydrolysis Nitrogen Content and pH Value Based on DWD

Aiming at the shortcomings of traditional methods for detecting the content of Alkaline Hydrolysis Nitrogen (AHN) and pH value in soil, such as time-consuming and labor-consuming, this paper proposes a rapid quantitative inversion method based on hyperspectral analysis of AHN content and pH value. This method uses db4 discrete wavelet denoising (DWD) and wavelet denoising normalization (DWD-N) to carry out Pearson correlation analysis, and two methods, Ridge regression and Partial Least Squares Regression (PLSR), were used to compare the accuracy of hyperspectral inversion of soil AHN content and pH value. Experiments have demonstrated that in the inversion of the AHN content prediction model, Ridge regression has a good modeling effect under the DWD-N model, where R2=0.647, RMSE=7.067mg/kg. PLSR has good prediction effect under DWD-N, where R2 is the highest of 0.792, RMSE is 3.438mg/kg; in the model inversion of pH prediction, the full-band PLSR modeling effect of pH value under DWD pretreatment is the best, which modeling set and the prediction set of R2 is 0.826 and 0.875, the RMSE is 0.217 mg/kg and 0.191 mg/kg respectively.

Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

The accuracy of wind power prediction directly affects the operation cost of power grid and is the result of power grid supply and demand balance. Therefore, how to improve the prediction accuracy of wind power is very important. In order to improve the prediction accuracy of wind power, a prediction model based on wavelet denoising and improved slime mold algorithm optimized support vector machine is proposed. The wavelet denoising algorithm is used to denoise the wind power data, and then the support vector machine is used as the prediction model. Because the prediction results of support vector machine are greatly affected by model parameters, an improved slime mold optimization algorithm with random inertia weight mechanism is used to determine the best penalty factor and kernel function parameters in support vector machine model. The effectiveness of the proposed prediction model is verified by using two groups actually collected wind power data. Seven prediction models are selected as the comparison model. Through the comparison between the predicted value and the actual value, the prediction error and its histogram distribution, the performance indicators, the Pearson’s correlation coefficient, the DM test, box-plot distribution, the results show that the proposed prediction model has high prediction accuracy.

A novel wavelet threshold denoising and deep belief network fault detection algorithm

For oil pipeline leakage fault detection problems, a novel negative pressure wave (NPW) leak detection method based on wavelet threshold denoising and deep belief network (Wavelet-DBN) is proposed. Firstly, the wavelet threshold denoising method is used to deal with the sample pressure signal, and the results of wavelet denoising with different wavelet basis functions and different decomposition levels are compared. The optimal parameters are selected for wavelet denoising and the characteristic information of a pipeline pressure signal is extracted. Secondly, in order to improve the accuracy of the pipeline leakage monitoring method based on NPW, the deep belief network algorithms are proposed to classify and identify the NPW sample signals. Finally, the sample data are collected from the industrial oil pipeline leakage experiment. The simulation experimental results show that the proposed method has a higher accuracy rate than other traditional machine learning methods, such as support vector machines, and reduces the false alarm rate of oil pipeline leakage monitoring.