Enhancement of the Flickermeter for Grid-Connected Wind Turbines

Distributed generators connected to the power system usually produce voltage fluctuations. For wind turbines connected to a grid, large changes in wind speed can cause voltage flicker at the point of common coupling. The measurement of voltage flicker caused only by wind turbines is difficult. The wind turbine under test is usually connected to a medium voltage point, in which other fluctuating loads may produce significant voltage disturbances at the wind turbine terminal where the measurement is made. Although the IEC 61400-21-1 standard specifies a method to evaluate voltage flicker caused by wind turbines, because of the complex algorithm and process of the IEC standard, there is currently a lack of measurement equipment that meets the IEC standard. In addition, some countries that use other voltage flicker standards, such as ΔV10, do not have suitable flicker measurements for wind turbines. Therefore, this study proposes an enhanced version of the IEC 61400-21-1 standard, which integrates the ΔV10 method, so that the proposed measurement system complies with the IEC and ΔV10 standards. In this study, the voltage flicker measurement system is successfully implemented, which can help engineers to predict the voltage flicker by wind turbines and assess whether a region or grid is suitable for installing wind turbines. Therefore, it can provide wind turbine companies with a quick assessment of voltage flicker to comply with the certification process.

Rationale for a New Outpatient Drug Therapy Algorithm in COVID-19 Patients Based on the Principle of «Multi-hit» Approach

The aim of the work was to justify the algorithm of outpatient drug therapy in patients with COVID-19, based on the principle of «Multi-hit» Approach. The algorithm is based on the published results of clinical studies and observations, authors’ own practical experience in the use and management of more than 4 thousand patients diagnosed with COVID-19 of varying severity during the 2020 pandemic. The article substantiates a complex algorithm for the treatment of outpatients with COVID-19, which includes etiotropic, pathogenetic, and symptomatic components of therapy with different mechanisms of action. The described approach is the 1st stage (outpatient) of a complex algorithm for managing patients with COVID-19. It has been successfully implemented in the system of outpatient care for patients with novel coronavirus infections in several leading medical institutions in Russia. The authors believe that the developed algorithm for providing outpatient drug therapy for COVID-19, based on the principle of multiple exposure, may be useful in real clinical practice of managing patients with coronavirus infection.

FPGA Implementation for Odor Identification with Depthwise Separable Convolutional Neural Network

The integrated electronic nose (e-nose) design, which integrates sensor arrays and recognition algorithms, has been widely used in different fields. However, the current integrated e-nose system usually suffers from the problem of low accuracy with simple algorithm structure and slow speed with complex algorithm structure. In this article, we propose a method for implementing a deep neural network for odor identification in a small-scale Field-Programmable Gate Array (FPGA). First, a lightweight odor identification with depthwise separable convolutional neural network (OI-DSCNN) is proposed to reduce parameters and accelerate hardware implementation performance. Next, the OI-DSCNN is implemented in a Zynq-7020 SoC chip based on the quantization method, namely, the saturation-flooring KL divergence scheme (SF-KL). The OI-DSCNN was conducted on the Chinese herbal medicine dataset, and simulation experiments and hardware implementation validate its effectiveness. These findings shed light on quick and accurate odor identification in the FPGA.

ASSESSMENT METHODS FOR BASIC NATIONAL BANKS

The article considers a systematic approach, which is based on the assessment of the basic banks as systemically important, determining the parameters of the development of the national economy and the possibility of risk. It compares the methods of evaluating large, basic banks that have a huge impact on the economies of the European Union (Germany, Great Britain), Japan and Russia. Revealed a more complex algorithm that estimates base for the national economy of banks in Russia and proposed to simplify the estimation procedure, limiting the scope of surveyed banks of their international activity by the Bank of Russia quantitative size of their monetary assets abroad. It is also proposed to evaluate the basic, system-forming banks not only annually, but also during periods of force majeure, since they serve as one of the tools for managing the national economy

Design of Reed-Solomon Encoder for Error Detection in DRAM Cells

This paper investigates the design of Reed Solomon (RS) encoder. Based on the message symbols, the RS encoder generates the code-word. By carrying out a polynomial division using Galois Field algebra, the parity symbols are calculated. Reed-Solomon codes are one of the most effective and effective non-binary error codes to detect and correct burst errors. This is the focus work for my dissertation to implement RS encoder and decoder that is a complex algorithm and it is used for the reliable memory operation in a system. The RS Encoder and decoder are design in structural modeling and develop the hardware. The sift and multiplier type divider is used for Encoder and Syndrome module design.

PERSONALIZED CALCULATOR FOR PREDICTION OF OPIOID-ASSOCIATED PHARMACORESISTANCE IN PATIENTS WITH PANCREASE CANCER

We studied the complex effect of genetic and non-genetic factors on the formation of opioid-associated resistance using machine learning methods in patients with chronic pain syndrome against the background of pancreatic cancer. Fifty-seven factors for predicting the realization of pharmacoresistance were studied in all examined Caucasian patients. The most significant predictive factors were determined and a software-analytical complex "Algorithm for assessing the significance of clinical and pathogenetic factors for predicting the safety of opioid therapy" was developed.

Anisotropic Diffusion with Deep Learning

We propose a deep learning framework for anisotropic diffusion which is based on a complex algorithm for a single image. Our network can be applied not only to a single image but also to multiple images. Also by blurring the image, the noise in the image is reduced. But the important features of objects remain. To apply anisotropic diffusion to deep learning, we use total variation for our loss function. Also, total variation is used in image denoising pre-process.[1] With this loss, our network makes successful anisotropic diffusion images. In these images, the whole parts are blurred, but edge and important features remain. The effectiveness of the anisotropic diffusion image is shown with the classification task.

A Robust Beat-to-Beat Artifact Detection Algorithm for Pulse Wave

With the rise of the concept of smart cities and healthcare, artificial intelligence helps people pay increasing attention to the health of themselves. People can wear a variety of wearable devices to monitor their physiological conditions. The pulse wave is a kind of physiological signal which is widely applied in the physiological monitoring system. However, the pulse wave is susceptible to artifacts, which prevents its popularization. In this work, we propose a novel beat-to-beat artifact detection algorithm, which performs pulse wave segmentation based on wavelet transform and then detects artifacts beat by beat based on the decision list. We verified our method on data acquired from different databases and compared with experts’ annotations. The segmentation algorithm achieved an accuracy of 96.13%. When it is applied to detect main peaks, the performance achieved an accuracy of 99.11%. After the previous segmentation algorithm, the artifact detection algorithm can detect beat-to-beat pulse waves and artifacts with an accuracy of 98.11%. The result indicated that the proposed method is robust for pulse waves of different patterns and could effectively detect the artifact without the complex algorithm. In summary, our proposed algorithm is capable of annotating pulse waves of various patterns and determining pulse wave quality. Since our method is developed and evaluated on the transmission-mode PPG data, it is more suitable for the devices and applications inside the hospitals instead of reflectance-mode PPG.