Bucknerhickey1992

This paper presents an innovative concept for the online application of Frequency Response Analysis (FRA). FRA is a well known technique that is applied to detect damage in electric machinery. As an offline technique, the machine under testing has to be removed from service-which may cause loss of production. Experimental adaptations of FRA to online operation are usually based on the use of passive high pass coupling-which, ideally, should provide attenuation to the grid voltage, and at the same time, allow the high frequency FRA signals to be injected at the machine. In practice, however, the passive coupling results in a trade-off between the required attenuation and the useful area obtained at the FRA spectra. This paper proposes the use of an active coupling system, based on power electronics, in order to cancel the grid voltage at the terminals of FRA equipment and allow its safe connection to an energized machine. The paper presents the basic concepts of FRA and the issue of online measurements. It also presents basic concepts about power electronics converters and the operating principles of the Modular Multilevel Converter, which enables the generation of an output voltage with low THD, which is important for tracking the grid voltage with minimum error.Wireless distributed storage is beneficial in the provision of reliable content storage and offloading of cellular traffic. In this paper, we consider a cellular device-to-device (D2D) underlay-based wireless distributed storage system, in which the minimum storage regenerating (MSR) coding combined with the partial downloading scheme is employed. To alleviate burdens on insufficient cellular resources and improve spectral efficiency in densely deployed networks, multiple storage devices can simultaneously use the same uplink cellular subchannel under the non-orthogonal multiple access (NOMA) protocol. Our objective is to minimize the total transmission power for content reconstruction, while guaranteeing the signal-to-interference-plus-noise ratio (SINR) constraints for cellular users by jointly optimizing power and subchannel allocation. To tackle the non-convex combinational program, we decouple the original problem into two subproblems and propose two low-complexity algorithms to efficiently solve them, followed by a joint optimization, implemented by alternately updating the solutions to each subproblem. The numerical results illustrate that our proposed algorithms are capable of performing an exhaustive search with lower computation complexity, and the NOMA-enhanced scheme provides more transmission opportunities for neighbor storage devices, thus significantly reducing the total power consumption.Organophosphates (OPs) are neurotoxic agents also used as pesticides that can permanently block the active site of the acetylcholinesterase (AChE). A robust and sensitive detection system of OPs utilising the enzyme mimic potential of the cysteamine capped gold nanoparticles (C-AuNPs) was developed. The detection assay was performed by stepwise addition of AChE, parathion ethyl (PE)-a candidate OP, acetylcholine chloride (ACh), C-AuNPs, and 3, 3', 5, 5'-tetramethylbenzidine (TMB) in the buffer solution. The whole sensing protocol completes in 30-40 min, including both incubations. The Transmission Electron Microscopy (TEM) results indicated that the NPs are spherical and have an average size of 13.24 nm. The monomers of C-AuNPs exhibited intense catalytic activity (nanozyme) for the oxidization of TMB, revealed by the production of instant blue colour and confirmed by a sharp peak at 652 nm. The proposed biosensor's detection limit and linear ranges were 5.8 ng·mL-1 and 11.6-92.8 ng·mL-1, respectively, for PE. The results strongly advocate that the suggested facile colorimetric biosensor may provide an excellent platform for on-site monitoring of OPs.Spirometer measurements can reflect cough strength but might not be routinely available for patients with severe neurological or medical conditions. A digital device that can record and help track abnormal cough sound changes serially in a noninvasive but reliable manner would be beneficial for monitoring such individuals. This report includes two cases of respiratory distress whose cough changes were monitored via assessments performed using recordings made with a digital device. The cough sounds were recorded using an iPad (Apple, Cupertino, CA, USA) through an embedded microphone. Cough sounds were recorded at the bedside, with no additional special equipment. The two patients were able to complete the recordings with no complications. The maximum root mean square values obtained from the cough sounds were significantly reduced when both cases were diagnosed with aspiration pneumonia. In contrast, higher values became apparent when the patients demonstrated a less severe status. Based on an analysis of our two cases, the patients' cough sounds recorded with a commercial digital device show promise as potential digital biomarkers that may reflect aspiration risk related to attenuated cough force. Serial monitoring aided the decision making to resume oral feeding. Future studies should further explore the clinical utility of this technique.This article presents a method for detecting rotational speed by LC (inductor-capacitor) wireless sensors. The sensing system consists of two identical LC resonant tanks. One is mounted on the rotating part and the other, as a readout circuit, is placed right above the rotating part. When the inductor on the rotating part is coaxially aligned with the readout inductor during rotation, the mutual coupling between them reaches the maximum, resulting in a peak amplitude induced at the readout LC tank. The period of the readout signal corresponds to the rotation speed. ADS (Advanced Design System) software was used to simulate and optimize the sensing system. A synchronous detection circuit was designed. The rotational speed of an electric was measured to validate this method experimentally, and the results indicated that the maximum error of the rotation speed from 16 rps to 41 rps was 0.279 rps.The chaotic squeak and rattle (S&R) vibrations in mechanical systems were classified by deep learning. The rattle, single-mode, and multi-mode squeak models were constructed to generate chaotic S&R signals. The repetition of nonlinear signals generated by them was visualized using an unthresholded recurrence plot and learned using a convolutional neural network (CNN). selleck screening library The results showed that even if the signal of the S&R model is chaos, it could be classified. The accuracy of the classification was verified by calculating the Lyapunov exponent of the vibration signal. The numerical experiment confirmed that the CNN classification using nonlinear vibration images as the proposed procedure has more than 90% accuracy. The chaotic status and each model can be classified into six classes.Lakes are integrators of past climate and ecological change. This information is stored in the sediment record at the lake bottom, and to make it available for paleoclimate research, potential target sites with undisturbed and continuous sediment sequences need to be identified. Different geophysical methods are suitable to identify, explore, and characterize sediment layers prior to sediment core recovery. Due to the high resolution, reflection seismic methods have become standard for this purpose. However, seismic measurements cannot always provide a comprehensive image of lake-bottom sediments, e.g., due to lacking seismic contrasts between geological units or high attenuation of seismic waves. Here, we developed and tested a complementary method based on water-borne electrical-resistivity tomography (ERT) measurements. Our setup consisted of 13 floating electrodes (at 5 m spacing) used to collect ERT data with a dipole-dipole configuration. We used a 1D inversion to adjust a layered-earth model, which facilitates the implementation of constraints on water depth, water resistivity, and sediment resistivity as a priori information. The first two parameters were readily obtained from the echo-sounder and conductivity-probe measurements. The resistivity of sediment samples can also be determined in the laboratory. We applied this approach to process ERT data collected on a lake in southern Mexico. The direct comparison of ERT data with reflection seismic data collected with a sub-bottom profiler (SBP) showed that we can significantly improve the sediment-thickness estimates compared to unconstrained 2D inversions. Down to water depths of 20 m, our sediment thickness estimates were close to the sediment thickness derived from collocated SBP seismograms. Our approach represents an implementation of ERT measurements on lakes and complements the standard lake-bottom exploration by reflection seismic methods.With a constant increase in the number of deployed satellites, it is expected that the current fixed spectrum allocation in satellite communications (SATCOM) will migrate towards more dynamic and flexible spectrum sharing rules. This migration is accelerated due to the introduction of new terrestrial services in bands used by satellite services. Therefore, it is important to design dynamic spectrum sharing (DSS) solutions that can maximize spectrum utilization and support coexistence between a high number of satellite and terrestrial networks operating in the same spectrum bands. Several DSS solutions for SATCOM exist, however, they are mainly centralized solutions and might lead to scalability issues with increasing satellite density. This paper describes two distributed DSS techniques for efficient spectrum sharing across multiple satellite systems (geostationary and non-geostationary satellites with earth stations in motion) and terrestrial networks, with a focus on increasing spectrum utilization and minimizing the impact of interference between satellite and terrestrial segments. Two relevant SATCOM use cases have been selected for dynamic spectrum sharing the opportunistic sharing of satellite and terrestrial systems in (i) downlink Ka-band and (ii) uplink Ka-band. For the two selected use cases, the performance of proposed DSS techniques has been analyzed and compared to static spectrum allocation. Notable performance gains have been obtained.Catechin is a major reactive substance involved in black tea fermentation. It has a determinant effect on the final quality and taste of made teas. In this study, we applied hyperspectral technology with the chemometrics method and used different pretreatment and variable filtering algorithms to reduce noise interference. After reduction of the spectral data dimensions by principal component analysis (PCA), an optimal prediction model for catechin content was constructed, followed by visual analysis of catechin content when fermenting leaves for different periods of time. The results showed that zero mean normalization (Z-score), multiplicative scatter correction (MSC), and standard normal variate (SNV) can effectively improve model accuracy; while the shuffled frog leaping algorithm (SFLA), the variable combination population analysis genetic algorithm (VCPA-GA), and variable combination population analysis iteratively retaining informative variables (VCPA-IRIV) can significantly reduce spectral data and enhance the calculation speed of the model.