ЗАВИСИМОСТЬ СПЕКТРА ПОТРЕБЛЯЕМОЙ МОЩНОСТИ ЭЛЕКТРОДВИГАТЕЛЯ НАСОСА ОТ ФИЗИЧЕСКИХ ПАРАМЕТРОВ МЕХАНИЗМА

In article dependence of the electric motor power consumption spectrum on physical parameters of the pump is proved. It is proved transitivity of transformation of methods of vibration-acoustic diagnostics in methods energy power diagnosticsУ статті доведено залежність спектру споживаємої потужності електродвигуна насосу від фізичніх параметрів механізму. Доведено транзитивність перетворення методів віброакустичної діагностики в методи енергодіагностики

WiRiS: Transformer for RIS-Assisted Device-Free Sensing for Joint People Counting and Localization using Wi-Fi CSI

Channel State Information (CSI) is widely adopted as a feature for indoor localization. Taking advantage of the abundant information from the CSI, people can be accurately sensed even without equipped devices. However, the positioning error increases severely in non-line-of-sight (NLoS) regions. Reconfigurable intelligent surface (RIS) has been introduced to improve signal coverage in NLoS areas, which can re-direct and enhance reflective signals with massive meta-material elements. In this paper, we have proposed a Transformer-based RIS-assisted device-free sensing for joint people counting and localization (WiRiS) system to precisely predict the number of people and their corresponding locations through configuring RIS. A series of predefined RIS beams is employed to create inputs of fingerprinting CSI features as sequence-to-sequence learning database for Transformer. We have evaluated the performance of proposed WiRiS system in both ray-tracing simulators and experiments. Both simulation and real-world experiments demonstrate that people counting accuracy exceeds 90%, and the localization error can achieve the centimeter-level, which outperforms the existing benchmarks without employment of RIS

Methods and apparatus for constructing and implementing a universal extension module for processing objects in a database

Methods and apparatus for providing a multi-tier object-relational database architecture are disclosed. In one illustrative embodiment of the present invention, a multi-tier database architecture comprises an object-relational database engine as a top tier, one or more domain-specific extension modules as a bottom tier, and one or more universal extension modules as a middle tier. The individual extension modules of the bottom tier operationally connect with the one or more universal extension modules which, themselves, operationally connect with the database engine. The domain-specific extension modules preferably provide such functions as search, index, and retrieval services of images, video, audio, time series, web pages, text, XML, spatial data, etc. The domain-specific extension modules may include one or more IBM DB2 extenders, Oracle data cartridges and/or Informix datablades, although other domain-specific extension modules may be used

A holo-spectral EEG analysis provides an early detection of cognitive decline and predicts the progression to Alzheimer’s disease

AimsOur aim was to differentiate patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) from cognitively normal (CN) individuals and predict the progression from MCI to AD within a 3-year longitudinal follow-up. A newly developed Holo-Hilbert Spectral Analysis (HHSA) was applied to resting state EEG (rsEEG), and features were extracted and subjected to machine learning algorithms.MethodsA total of 205 participants were recruited from three hospitals, with CN (n = 51, MMSE > 26), MCI (n = 42, CDR = 0.5, MMSE ≥ 25), AD1 (n = 61, CDR = 1, MMSE < 25), AD2 (n = 35, CDR = 2, MMSE < 16), and AD3 (n = 16, CDR = 3, MMSE < 16). rsEEG was also acquired from all subjects. Seventy-two MCI patients (CDR = 0.5) were longitudinally followed up with two rsEEG recordings within 3 years and further subdivided into an MCI-stable group (MCI-S, n = 36) and an MCI-converted group (MCI-C, n = 36). The HHSA was then applied to the rsEEG data, and features were extracted and subjected to machine-learning algorithms.Results(a) At the group level analysis, the HHSA contrast of MCI and different stages of AD showed augmented amplitude modulation (AM) power of lower-frequency oscillations (LFO; delta and theta bands) with attenuated AM power of higher-frequency oscillations (HFO; beta and gamma bands) compared with cognitively normal elderly controls. The alpha frequency oscillation showed augmented AM power across MCI to AD1 with a reverse trend at AD2. (b) At the individual level of cross-sectional analysis, implementation of machine learning algorithms discriminated between groups with good sensitivity (Sen) and specificity (Spec) as follows: CN elderly vs. MCI: 0.82 (Sen)/0.80 (Spec), CN vs. AD1: 0.94 (Sen)/0.80 (Spec), CN vs. AD2: 0.93 (Sen)/0.90 (Spec), and CN vs. AD3: 0.75 (Sen)/1.00 (Spec). (c) In the longitudinal MCI follow-up, the initial contrasted HHSA between MCI-S and MCI-C groups showed significantly attenuated AM power of alpha and beta band oscillations. (d) At the individual level analysis of longitudinal MCI groups, deploying machine learning algorithms with the best seven features resulted in a sensitivity of 0.9 by the support vector machine (SVM) classifier, with a specificity of 0.8 yielded by the decision tree classifier.ConclusionIntegrating HHSA into EEG signals and machine learning algorithms can differentiate between CN and MCI as well as also predict AD progression at the MCI stage

Controlled three-dimensional polystyrene micro- and nano-structures fabricated by three- dimensional electrospinning

The combination of electrospinning with extrusion based 3D printing technology opens new pathways for micro- and nanofabrication, which can be applied in a wide range of applications. This simple and inexpensive method has been proven to fabricate 3D fibrous polystyrene structures with controlled morphology and micro- to nano-scale fibers diameter. The controllable movement of the nozzle allows precise positioning of the deposition area of the fibers during electrospinning. A programmed circular nozzle pattern results in the formation of controllable 3D polystyrene designed shapes with fiber diameters down to 550 nm. The assembly of the fibrous structures starts instantaneously, and a 4 cm tall and 6 cm wide sample can be produced within a 10 minutes electrospinning process. The product exhibits high stability at ambient conditions. The shape, size, and thickness of fibrous polystyrene structures can be easily controlled by tuning the process parameters. It is assumed that the build-up of 3D fibrous polystyrene structures strongly depends on charge induction and polarization of the electrospun fibers

Nonlinear analysis of wiggler taper, mode competition, and space-charge effects for a 280-GHz free electron laser

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