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Full-Text Articles in Engineering
Predicting Insulin Pump Therapy Settings, Riccardo L. Ferraro, David Grijalva, Alex Trahan
Predicting Insulin Pump Therapy Settings, Riccardo L. Ferraro, David Grijalva, Alex Trahan
SMU Data Science Review
Millions of people live with diabetes worldwide [7]. To mitigate some of the many symptoms associated with diabetes, an estimated 350,000 people in the United States rely on insulin pumps [17]. For many of these people, how effectively their insulin pump performs is the difference between sleeping through the night and a life threatening emergency treatment at a hospital. Three programmed insulin pump therapy settings governing effective insulin pump function are: Basal Rate (BR), Insulin Sensitivity Factor (ISF), and Carbohydrate Ratio (ICR). For many people using insulin pumps, these therapy settings are often not correct, given their physiological needs. While …
Forestry Fire Spatial Diffusion Model Based On Integration Of Multi-Agent Algorithm With Cellular Automata, Zhao Di, Zhisheng Xu
Forestry Fire Spatial Diffusion Model Based On Integration Of Multi-Agent Algorithm With Cellular Automata, Zhao Di, Zhisheng Xu
Journal of System Simulation
Abstract: In view of the existing ultrasonic detection signal attenuation, directional difference, complex propagation paths, and characteristics of the components of complex, a kind of intelligent concrete defect nondestructive detection algorithm based on multi resolution singular entropy was put forward. By using the wavelet algorithm, the ultrasonic signal was decomposed into high, low frequency components of multi scales for each component, then each component was decomposed by singular spectrum analysis, at the same time using the information entropy theory, the calculation of singular entropy as the characteristic defect detection value, using the GA-SVM algorithm was used to train the singular …
Probabilistic Dynamic Security Assessment Of Large Power Systems Using Machine Learning Algorithms, Sevda Jafarzadeh, Veysel Murat İstemi̇han Genç
Probabilistic Dynamic Security Assessment Of Large Power Systems Using Machine Learning Algorithms, Sevda Jafarzadeh, Veysel Murat İstemi̇han Genç
Turkish Journal of Electrical Engineering and Computer Sciences
Arrhythmia, also known as dysrhythmia, is a condition involving an irregular heartbeat. A problem in the heart may cause problems in other organs, and as time passes, this will lead to more severe problems. Arrhythmia must be detected at an early stage to prevent such a problem occurring in the heart. Detection of arrhythmia from an electrocardiogram is an easy method that does not need much equipment and does not harm the patient. The purpose of this research is to find a faster and more accurate system to classify nine classes of arrhythmia. The St. Petersburg Institute of Cardiological Technics …
Bagged Tree Classification Of Arrhythmia Using Wavelets For Denoising, Compression, And Feature Extraction, Özgür Tomak, Temel Kayikçioğlu
Bagged Tree Classification Of Arrhythmia Using Wavelets For Denoising, Compression, And Feature Extraction, Özgür Tomak, Temel Kayikçioğlu
Turkish Journal of Electrical Engineering and Computer Sciences
Arrhythmia, also known as dysrhythmia, is a condition involving an irregular heartbeat. A problem in the heart may cause problems in other organs, and as time passes, this will lead to more severe problems. Arrhythmia must be detected at an early stage to prevent such a problem occurring in the heart. Detection of arrhythmia from an electrocardiogram is an easy method that does not need much equipment and does not harm the patient. The purpose of this research is to find a faster and more accurate system to classify nine classes of arrhythmia. The St. Petersburg Institute of Cardiological Technics …
Region Characteristics-Based Fusion Of Spatial And Transform Domain Image Denoising Methods, Rajiv Verma, Rajoo Pandey
Region Characteristics-Based Fusion Of Spatial And Transform Domain Image Denoising Methods, Rajiv Verma, Rajoo Pandey
Turkish Journal of Electrical Engineering and Computer Sciences
Nonlocal means (NLM)- and wavelet-based image denoising methods have drawn much attention in image processing due to their effectiveness and simplicity. The performance of these algorithms varies according to region characteristics in an image. For example, NLM performs well for smooth regions due to deployment of redundancy available in images, whereas wavelet-based approaches may preserve key image features by controlling the degree of threshold for shrinking the noisy coefficients. This paper presents a simple novel approach that estimates an original image by simply taking the weighted average of the denoised images pixel values obtained by NLM and wavelet thresholding schemes …
Application Of Acf-Wavelet Feature Extraction For Classification Of Some Artificial Pd Models Of Power Transformer, Vahid Parvin Darabad
Application Of Acf-Wavelet Feature Extraction For Classification Of Some Artificial Pd Models Of Power Transformer, Vahid Parvin Darabad
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, 7 different artificial partial discharge (PD) models of power transformer defect types are built in a high voltage laboratory, and then PD signals are recorded in the whole power frequency cycle to develop a preliminary PD knowledge base. A specific technique is used to extract single PD events from the recorded knowledge base. By application of wavelet transform, different decomposition levels of extracted PD signals are investigated. Feature extraction is performed by application of statistic moments on the autocorrelation function of PD signal decompositions. "Best" features are selected based on the F-test. To evaluate the performance of …
Statistical Features-Based Comparison Of Analysis And Synthesis Of Normal And Epileptic Electroencephalograms For Various Wavelets, Revati Shriram, Mahalingam Sundhararajan, Supriya Shete, Nivedita Daimiwal
Statistical Features-Based Comparison Of Analysis And Synthesis Of Normal And Epileptic Electroencephalograms For Various Wavelets, Revati Shriram, Mahalingam Sundhararajan, Supriya Shete, Nivedita Daimiwal
Turkish Journal of Electrical Engineering and Computer Sciences
An electroencephalogram (EEG) is an electrical signal in microvolts captured noninvasively from the brain, which provides important and unique information about the brain. The frequency of an EEG signal lies between 0 and 100 Hz. Decomposition of an EEG signal into various bands such as alpha, beta, delta, theta, and gamma is essential in seizure-related studies. EEGs play a key role in the diagnosis of epileptic seizures and neurological disorders. In this paper, multiple wavelet families for decomposition and reconstruction are explored and are compared based on risk functions and reconstruction measures. While dealing with the wavelets it is a …
A Comparative Study Of Denoising Semg Signals, Ulvi̇ Başpinar, Volkan Yusuf Şenyürek, Bariş Doğan, Hüseyi̇n Selçuk Varol
A Comparative Study Of Denoising Semg Signals, Ulvi̇ Başpinar, Volkan Yusuf Şenyürek, Bariş Doğan, Hüseyi̇n Selçuk Varol
Turkish Journal of Electrical Engineering and Computer Sciences
Denoising of surface electromyography (sEMG) signals plays a vital role in sEMG-based mechatronics applications and diagnosis of muscular diseases. In this study, 3 different denoising methods of sEMG signals, empirical mode decomposition, discrete wavelet transform (DWT), and median filter, are examined. These methods are applied to 5 different levels of noise-added synthetic sEMG signals. For the DWT-based denoising technique, 40 different wavelet functions, 4 different threshold-selection-rules, and 2 threshold-methods are tested iteratively. Three different window-sized median filters are applied as well. The SNR values of denoised synthetic signals are calculated, and the results are used to select DWT and median …