Simulation of a High-Frequency Link of Phase Comparison Protection of Transmission Lines for Optimization its Settings

The objective of this work is to determine the influence of high-frequency link (HFL) on functioning of phase comparison protection (PCP) through the usage of detailed models that take into account specific features of the PCP and processes in HFL. The article describes the case of the most common variant of the high frequency signal transmission by using a scheme 'phase-to-ground'. To verify suggested solution the developed mathematical model of HFL has been studied. Presented simulation results confirm the effectiveness of the proposed approach for solving the problem

Experimental and numerical results of active flow control on a highly loaded stator cascade

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.This article presents experimental and numerical results for a compressor cascade with active flow control. Steady and pulsed blowing has been used to control the secondary flow and separation characteristics of a highly loaded controlled diffusion airfoil. Investigations were performed at the design incidence for blowing ratios from approximately 0.7 to 3.0 (jet-to-inlet velocity) and a Reynolds number of 840 000 (based on axial chord and inlet velocity). Detailed flow field data were collected using a five-hole pressure probe, pressure taps on the blade surfaces, and time-resolved Particle Image Velocimetry. Unsteady Reynolds-averaged Navier–Stokes simulations were performed for a wide range of flow control parameters. The experimental and numerical results are used to understand the interaction between the jet and the passage flow. The benefit of the flow control on the cascade performance is weighted against the costs of the actuation by introducing an efficiency which takes the presence of the jets into account.DFG, SFB 557, Beeinflussung komplexer turbulenter Scherströmunge

Cd[B2(SO4)4] and H2[B2(SO4)4] – a phyllosilicate-analogous borosulfate and its homeotypic heteropolyacid

Borosulfates consist of heteropolyanionic networks of corner-shared (SO4)- and (BO4)-tetrahedra charge compensated by metal or non-metal cations. The anionic substructures differ significantly, depending on the different branching of the silicate-analogous borosulfate building blocks. However, only one acid has been characterized by single crystal X-ray diffraction so far. Herein, we present H-2[B-2(SO4)(4)] as the first phyllosilicate analogue representative, together with the homeotypic representative Cd[B-2(SO4)(4)]. The latter can be considered the cadmium salt of the former. Their crystal structures and crystallographic relationship are elucidated. For H-2[B-2(SO4)(4)], the bonding situation is examined using Hirshfeld-surface analysis. Further, the optical and thermal properties of Cd[B-2(SO4)(4)] are investigated by FTIR and UV-Vis spectroscopy, thermogravimetry, as well as temperature-programmed powder X-ray diffraction

ВДОСКОНАЛЕННЯ УПРАВЛІННЯ ФІНАНСУВАННЯМ ІННОВАЦІЙНО-ІНВЕСТИЦІЙНОЇ ДІЯЛЬНОСТІ НА ПРОМИСЛОВИХ ПІДПРИЄМСТВАХ

Метою статті є дослідження особливостей фінансових аспектів управління інноваційно-інвестиційною діяльністю промислових підприємств і визначення шляхів підвищення ефективності фінансування ІІДРозкрито сутність і зміст системи управління фінансуванням інноваційно-інвестиційної діяльності на промислових підприємства

Sleep stage classification using spectral analyses and support vector machine algorithm on C3- and C4-EEG signals [Abstract]

Introduction Sleep stage classification currently relies largely on visual classification methods. We tested a new pipeline for automated offline classification based upon power spectrum at six different frequency bands. The pipeline allowed sleep stage classification and provided whole-night visualization of sleep stages. Materials and methods 102 subjects (69 male; 53.74 ± 12.4 years) underwent full-night polysomnography. The recording system included C3- and C4-EEG channels. All signals were measured at sampling rate of 200 Hz. Four epochs (30 seconds each) of each sleep stage (N1, N2, N3, REM, awake) were marked in the visually scored recordings of each one of the 102 patients. Scoring of sleep stages was performed according to AASM 2007-criteria. In total 408 epochs for each sleep stage were included in the sleep stage classification analyses. Recordings of all these epochs were fed into the pipeline to estimate the power spectrum at six different frequency bands, namely from very low frequency (VLF, 0.1-1 Hz) to gamma frequency (30-50 Hz). The power spectrum was measured with a method called multitaper method. In this method the spectrum is estimated by multiplying the data with K windows (i.e tapers).The estimated parameters were given as input to the support vector machine (SVM) algorithm to classify the five different sleep stages based on the mean power amplitude estimated from six different frequency bands. The SVM algorithm was trained with 51 subjects and the testing was done with the other 51 subjects. In order to avoid bias of the training dataset, a 10-fold cross validation was additionally done to check the performance of the SVM algorithm Results The estimated testing accuracy of prediction of the sleep stages was 84.1% for stage N1 using the mean power amplitude from the delta frequency band. Accuracy was 67.8% for stage N2 from the delta frequency band and 74.9% for stage N3 from the VLF. Accuracy was 79.7% for REM stage from the delta frequency band and 84,8% for the wake stage from the theta frequency band. Conclusions We were able to successfully classify the sleep stages using the mean power amplitude at six different frequency bands separately and achieved up to 85% accuracy using the electrophysiological EEG signals. The delta and theta frequency bands gave the best accuracy of classification among all sleep stages

EEG-EMG-coherence in SDB patients with utilization of a support vector machine-algorithm [Poster Abstract]

Background We investigated whether the EEG-EMG-coherence allows a differentiation between patients with sleep-disordered breathing (SDB) without OSA and SDB-patients with mild, moderate or severe OSA. Methods Polysomnographic recordings of 102 patients with SDB (33 female; age: 53,± 12,4 years) were analyzed with the multitaper coherence method (MTM). Recordings contained 2 EEG-channels (C3 and C4) and a chin EMG-channel for one night. Four epochs (each 30 seconds, classified manually by AASM 2007 criteria) of each sleep stage were marked (1632 epochs in total), which were included in the classification analysis. The collected data sets were supplied to the support vector machine (SVM) algorithm to classify OSA severity. Twenty patients had a mild (RDI ≥10/h and < 15/h), 30 patients had a moderate (RDI ≥15/h and < 30/h) and 27 patients had a severe OSA (RDI ≥30/h). 25 patients had a RDI < 10/h. The AUC (area under the curve) value was calculated for each receiver operator curve (ROC) curve. Results EEG-EMG coherence was able to distinguish between the SDB-patients without OSA and SDB-patients with OSA in each of the 3 severity groups using an SVM algorithm. In mild OSA, the AUC was 0.616 (p = 0.024), in moderate OSA the AUC was 0.659 (p = 0.003), and in severe OSA the AUC was 0.823 (p < 0.001). Conclusions SDB patients with OSA can be differentiated from SDB patients without OSA on the basis of EEG-EMG coherence by using the Multitaper Coherence Method (MTM) and SVM algorithm

Longitudinal grey and white matter changes in frontotemporal dementia and Alzheimer's disease

Behavioural variant frontotemporal dementia (bvFTD) and Alzheimer's disease (AD) dementia are characterised by progressive brain atrophy. Longitudinal MRI volumetry may help to characterise ongoing structural degeneration and support the differential diagnosis of dementia subtypes. Automated, observer-independent atlas-based MRI volumetry was applied to analyse 102 MRI data sets from 15 bvFTD, 14 AD, and 10 healthy elderly control participants with consecutive scans over at least 12 months. Anatomically defined targets were chosen a priori as brain structures of interest. Groups were compared regarding volumes at clinic presentation and annual change rates. Baseline volumes, especially of grey matter compartments, were significantly reduced in bvFTD and AD patients. Grey matter volumes of the caudate and the gyrus rectus were significantly smaller in bvFTD than AD. The bvFTD group could be separated from AD on the basis of caudate volume with high accuracy (79% cases correct). Annual volume decline was markedly larger in bvFTD and AD than controls, predominantly in white matter of temporal structures. Decline in grey matter volume of the lateral orbitofrontal gyrus separated bvFTD from AD and controls. Automated longitudinal MRI volumetry discriminates bvFTD from AD. In particular, greater reduction of orbitofrontal grey matter and temporal white matter structures after 12 months is indicative of bvFTD

Анализ разработки и выбора способа эксплуатации Сугмутского нефтяного месторождения (ЯНАО)

Целью данной выпускной квалификационной работы является изучение состояния разработки месторождения, степени реализации проектных решений и предложения по совершенствованию процесса разработки месторожденияThe purpose of this final qualifying work is to study the state of development, the degree of implementation of design solutions and proposals for improving the development of field