This study presents a technique to identify the vibration characteristics in power transmission towers and to detect the potential structural damages. This method is based on the curvature of the mode shapes coupled with a continuous wavelet transform. The elaborated numerical method is based on signal processing of the output that resulted from ambient vibration. This technique benefits from a limited number of sensors, which makes it a cost-effective approach compared to others. The optimal spatial location for these sensors is obtained by the minimization of the non-diagonal entries in the modal assurance criterion (MAC) matrix. The Hilbert-Huang transform was also used to identify the dynamic anatomy of the structure. In order to simulate the realistic condition of the measured structural response in the field condition, a 10% noise is added to the response of the numerical model. Four damage scenarios were considered, and the potential damages were identified using wavelet transform on the difference of mode shapes curvature in the intact and damaged towers. Results show a promising accuracy considering the small number of applied sensors. This study proposes a low-cost and feasible technique for structural health monitoring.

https://www.mdpi.com/1424-8220/20/6/1731/pdf

Vibration Anatomy and Damage Detection in Power Transmission Towers with Limited Sensors.

In this article, an automated heartbeat classification has been proposed to prevent the growing threats of cardiovascular diseases around the world. The MIT-BIH arrhythmia database has been used for the training and testing of the proposed approach. The database contains 90 095 normal (N), 2781 supraventricular (SVEB), 7008 ventricular (VEB), 802 fusions (F), and 15 unclassified (Q) beats each of 30 min duration. Total 61 features have been extracted using the time-series feature extraction library (TSFEL). Feature selection or reduction methods applied are feature scaling, removal of highly correlated and low variance features, and random forest (RF) recursive feature elimination. The methodological novelty of this study is mainly the incorporation of TSFEL during feature extractions, synthetic minority oversampling technique to create a balanced dataset, and an ensemble of RF and support vector machine (SVM) using weighted majority algorithm for heartbeat classification to improve the results. Grid search has been performed to optimize the hyperparameters of RF and SVM classifiers. A final evaluation has been carried out considering a “subject-specific” scheme. The sensitivity that our approach has achieved for the arrhythmic heartbeat classes is as follows: N: 99.50%, SVEB (S): 74.20%, VEB (V): 94.22%, F: 73.21%, and Q: 0%. The corresponding positive predictive values are N: 98.67%, SVEB (S): 90.09%, VEB (V): 95.95%, F: 88.35%, and Q: 0%. In comparison with machine learning and deep learning based state-of-the-art approaches, significant improvement in efficiency has been found. Impact Statement— Automated classifications of heartbeats from ECG signals are very much required to speed up the diagnosis of cardiovascular diseases (CVDs). Our study demonstrates 98.21% accuracy compared to existing state-of-the-art approaches. This study impacts the intensive health care system by offering an easy, quick, and cost-effective diagnosis of CVDs. Besides, remote patients can also receive the treatments timely. Early intervention and prevention of CVDs can prevent almost 30% of the total death every year. The proposed approach has a societal and economic impact too, as the health, welfare, and productivity of the citizens as well as of the country will be improved significantly. Approximately 2% of gross domestic product (GDP) will be improved across low- and middle-income countries as the proposed approaches will reduce the direct (treatment cost) and indirect costs (loss in productivities at workplaces) associated with CVDs.

Arrhythmic Heartbeat Classification Using Ensemble of Random Forest and Support Vector Machine Algorithm

Energy Storage Systems (EES) are key to further increase the penetration in energy grids of intermittent renewable energy sources, such as wind, by smoothing out power fluctuations. In order this to be economically feasible; however, the ESS need to be sized correctly and managed efficiently. In the study, the use of discrete wavelet transform (Daubechies Db4) to decompose the power output of utility-scale wind turbines into high and low-frequency components, with the objective of smoothing wind turbine power output, is discussed and applied to four-year Supervisory Control And Data Acquisition (SCADA) real data from multi-MW, on-shore wind turbines provided by the industrial partner. Two main research requests were tackled: first, the effectiveness of the discrete wavelet transform for the correct sizing and management of the battery (Li-Ion type) storage was assessed in comparison to more traditional approaches such as a simple moving average and a direct use of the battery in response to excessive power fluctuations. The performance of different storage designs was compared, in terms of abatement of ramp rate violations, depending on the power smoothing technique applied. Results show that the wavelet transform leads to a more efficient battery use, characterized by lower variation of the averaged state-of-charge, and in turn to the need for a lower battery capacity, which can be translated into a cost reduction (up to −28%). The second research objective was to prove that the wavelet-based power smoothing technique has superior performance for the real-time control of a wind park. To this end, a simple procedure is proposed to generate a suitable moving window centered on the actual sample in which the wavelet transform can be applied. The power-smoothing performance of the method was tested on the same time series data, showing again that the discrete wavelet transform represents a superior solution in comparison to conventional approaches.

Discrete Wavelet Transform for the Real-Time Smoothing of Wind Turbine Power Using Li-Ion Batteries

Stroke is considered as a major cause of death and neurological disorders commonly associated with elderly people. Electrocardiogram (ECG) signals are used as a powerful tool in diagnosing stroke, and the analysis of ECG signals has become the focus of stroke research. ECG changes and autonomic dysfunction are reportedly seen in patients with stroke. This study aimed to analyze the ECG features and develop a classification model with highly ranked ECG features as input variables based on machine-learning techniques for diagnosing stroke disease. The study included 52 stroke patients (mean age 72.7 years, 63% male) and 80 control subjects (mean age 75.5 years, 39% male) for a total of 132 elderly subjects. Resting ECG signals in the lying down position are measured using the BIOPAC MP150 system. The ECG signals are denoised using the discrete wavelet transform (DWT) method, and the features such as heart rate variability (HRV), indices of time and spectral domains and statistical and impulsive metrics, in addition to fiducial features, are extracted and analyzed. Our results showed that the values of the HRV variables were lower in the stroke group, revealing autonomic dysfunction in stroke patients. A statistically significant difference was observed in low-frequency (LF)/high-frequency (HF), time interval measured after the S wave to the beginning of the T wave (ST) and time interval measured from the beginning of the Q wave to the end of the T wave (QT) (p < 0.05) between the groups. Our study also highlighted some of the risk factors of stroke, such as age, male sex and dyslipidemia (p < 0.05), that are statistically significant. The k-nearest neighbors (KNN) model showed the highest classification results (accuracy 96.6%, precision 94.3%, recall 99.1% and F1-score 96.6%) than the random forest, support vector machine (SVM), Naive Bayes and logistic regression models. Thus, our study reported some of the notable ECG changes in the study participants and also indicated that ECG could aid in diagnosing stroke disease.

Evaluation of ECG Features for the Classification of Post-Stroke Survivors with a Diagnostic Approach

Divers’ health state after underwater activity can be assessed after the immersion using precordial echo Doppler examination. An audio analysis of the acquired signals is performed by specialist doctors to detect circulating gas bubbles in the vascular system and to evaluate the decompression sickness risk. Since on-site medical assistance cannot always be guaranteed, we propose a system for automatic emboli detection using a custom portable device connected to the echo Doppler instrument. The empirical mode decomposition method is used to develop a real-time algorithm able to automatically detect embolic events and, consequently, assess the decompression sickness risk according to the Spencer’s scale. The proposed algorithm has been tested according to an experimental protocol approved by the Divers Alert Network. It involved 30 volunteer divers and produced 37 echo Doppler files useful for the algorithm’s performances evaluation. The results obtained by the proposed emboli detection algorithm (83% sensitivity and 76% specificity) make the system particularly suitable for real-time evaluation of the decompression sickness risk level. Furthermore, the system could also be used in continuous monitoring of hospitalized patients with embolic risks such as post surgery ones.

https://www.mdpi.com/2079-9292/8/8/824/pdf

An EMD-Based Algorithm for Emboli Detection in Echo Doppler Audio Signals

Development and improvement of a mathematical model for a large-scale analysis based on the Daubechies discrete wavelet transform will be implemented in an algebraic system possessing a property of ring and field suitable for speech signals processing. Modular codes are widely used in many areas of modern information technologies. The use of these non-positional codes can provide high-speed data processing. Therefore, these algebraic systems should be used in the algorithms of digital processing of signals, which are characterized by processing large amounts of data in real time. In addition, modular codes make it possible to implement large-scale signal processing using the wavelet transform. The paper discusses examples of the Daubechies wavelet transform application. Integer processing, presented in the paper, will reduce the number of rounding errors when processing the speech signals.

/pdf/an-algorithm-of-daubechies-wavelet-transform-in-the-final-4lxxuep8t6.pdf

Artem Gapochkin

Papers

An Algorithm of Daubechies Wavelet Transform in the Final Field When Processing Speech Signals