Explore the vast range of titles available.

Electricity has a crucial function in contemporary civilization. The power grid must be stable to ensure the efficiency and dependability of electrical equipment. This implies that the high-voltage equipment at the substation must be reliably operated. As a result, the appropriate and dependable use of systems to monitor the operating status of high-voltage electrical equipment has recently gained attention. Partial discharge (PD) analysis is one of the most promising solutions for monitoring and diagnosing potential problems in insulation systems. Noise is a major challenge in diagnosing and detecting defects when using this measurement. This study aims to denoise PD signals using a data decomposition method, improved complete ensemble empirical mode decomposition with adaptive noise algorithm, combined with statistical significance test to increase noise reduction efficiency and to derive and visualize the Hilbert spectrum of the input signal in time-frequency domain after filtering the noise. In the PD signal analysis, both artificial and experimental signals were used as input signals in the decomposition method. For these signals, this study has yielded significant improvement in the denoising and the PD detecting process indicated by statistical measures. Thus, the signal decomposition by using the proposed method is proven to be a useful tool for diagnosing the PD on high voltage equipment.

In the context of geo-infrastructures and specifically tunneling projects, analyzing the large-scale sensor-based measurement-while-drilling (MWD) data plays a pivotal role in assessing rock engineering conditions. However, handling the big MWD data due to multiform stacking is a time-consuming and challenging task. Extracting valuable insights and improving the accuracy of geoengineering interpretations from MWD data necessitates a combination of domain expertise and data science skills in an iterative process. To address these challenges and efficiently normalize and filter out noisy data, an automated processing approach integrating the stepwise technique, mode, and percentile gate bands for both single and peer group-based holes was developed. Subsequently, the mathematical concept of a novel normalizing index for classifying such big datasets was also presented. The visualized results from different geo-infrastructure datasets in Sweden indicated that outliers and noisy data can more efficiently be eliminated using single hole-based normalizing. Additionally, a relational unified PostgreSQL database was created to store and automatically transfer the processed and raw MWD as well as real time grouting data that offers a cost effective and efficient data extraction tool. The generated database is expected to facilitate in-depth investigations and enable application of the artificial intelligence (AI) techniques to predict rock quality conditions and design appropriate support systems based on MWD data.

In this paper, a scheme for generating high-quality tunable microwave-frequency combs (MFCs) is proposed. The proposed scheme is based on an initially non-flat MFC generated by a directly modulated laser operating in gain-switching status. Filtering operations are used to increase the flatness of the MFC. Concretely, by employing an optical bandpass filter and a two-tap negative-coefficient microwave photonic filter, the flatness of the MFC is significantly optimized. In the experiment, MFCs with adjustable comb spacing from 0.5 GHz to 1.6 GHz and bandwidths ranging from 0 to 26.5 GHz are generated. The flatness is better than ±2.5 dB for the MFC. The proposed scheme provides a simple, efficient, and high-performance solution for generating MFCs, making it a promising candidate for various applications requiring high-quality MFC sources.

Fire significantly contributes to the distribution, composition, and functioning of ecosystems. It is also among the most damaging disturbances, exacerbated by increasing fire intensity and extent due to climate change. However, some ecosystems, like Mediterranean scrublands, recover quickly due to species’ adaptations to frequent fire regimes. This study investigates the community assembly processes during a secondary succession following the fire in a dune system within Doñana Natural Park, Southern Spain. To achieve this, three shrub communities were characterised along a coast-to-inland gradient over two consecutive years using a Non-Metric Multidimensional Scaling ordination. Then, a set of ecophysiological and structural traits of the dominant shrub species were analysed through a Principal Component Analysis, and correlation analyses were performed to assess the relationships between the three communities and the studied traits. Finally, to investigate the spatial structure of the shrub communities, a co-occurrence network was constructed and Moran’s I analyses were conducted. The results revealed that natural secondary succession has restored shrub communities along the gradient to states similar to those existing before the fire, indicating a consistent recovery pattern. Although species composition was comparable across zones, community spatial structure and several traits varied from coastal to interior areas, with more complex co-occurrence networks observed inland. Overall, the results underline the significance of environmental filters and species interactions in shaping community assembly during secondary succession after fire. Furthermore, they highlight that post fire community responses can be predicted through mechanisms linking the regional species pool, regeneration traits, and physical heterogeneity.

A high-efficiency inverted-type CH3NH3PbI3 (MAPbI3) solar cell was fabricated by using a ultrathin poly[3-(4-carboxybutyl)thiophene-2,5-diyl]-Na (P3CT-Na) film as the hole transport layer. The averaged power conversion efficiency (PCE) can be largely increased from 11.72 to 18.92% with a double-filtering process of the P3CT-Na solution mainly due to the increase in short-circuit current density (JSC) from 19.43 to 23.88 mA/cm2, which means that the molecular packing structure of P3CT-Na thin film can influence the formation of the MAPbI3 thin film and the contact quality at the MAPbI3/P3CT-Na interface. Zeta potentials, atomic-force microscopic images, absorbance spectra, photoluminescence spectra, X-ray diffraction patterns, and Raman scattering spectra are used to understand the improvement in the JSC. Besides, the light intensity-dependent and wavelength-dependent photovoltaic performance of the MAPbI3 solar cells shows that the P3CT-Na thin film is not only used as the hole transport layer but also plays an important role during the formation of a high-quality MAPbI3 thin film. It is noted that the PCE values of the best P3CT-Na based MAPbI3 solar cell are higher than 30% in the yellow-to-near infrared wavelength range under low light intensities. On the other hand, it is predicted that the double-filtering method can be readily used to increase the PCE of polymer based solar cells.

At the Faculty of Engineering, University of Debrecen, we have long been engaged in the design and development of self-constructed, predominantly electric, single-seat prototype race cars. To enhance the efficiency of both vehicle design and competitive performance, the authors previously developed a modular technical data optimization software. This tool comprises two key modules: a vehicle dynamics simulation program that derives driving dynamics from technical specifications (parameters) and an optimization module that fine-tunes these parameters for various racing scenarios. However, the large number of input variables often renders the optimization process computationally intensive and time-consuming. To address this challenge, we introduce a novel filtering process designed to streamline the optimization process. This method systematically identifies and excludes parameters whose uncertainties exert minimal influence on the simulation outcomes. This approach significantly reduces computational overhead, thereby accelerating the optimization process.

PurposeThe aim of this study was to test the validity of a method using an inertial measurement unit for estimating activity-related energy expenditure (AEE) during walking in middle-aged adults.MethodsTwenty healthy middle-aged participants completed different treadmill walking trials with an inertial measurement unit adhered to their lower back. Gas exchange was monitored with indirect calorimetry. Mechanical data were used to estimate AEE from an algorithm developed by Bouten et al. (Med Sci Sport Exer 26(12):1516–1523, 1994). Three methods for removing the gravitational component were proposed and tested: mean subtraction method (MSM), high-pass filter method (HPM) and free acceleration method (FAM).ResultsThe three methods did not differ significantly from the indirect calorimetry [bias = − 0.08 kcal min−1; p = 0.47 (MSM), bias = − 0.08 kcal min−1; p = 0.48 (HPM) and bias = − 0.15 kcal min−1; p = 0.23 (FAM)]. Mean root mean square errors were 0.43, 0.42 and 0.51 kcal min−1 for MSM, HPM and FAM, respectively.ConclusionThis study proposed an accurate method for estimating AEE in middle-aged adults for a large range of walking intensities, from slow to brisk walking, based on Bouten’s algorithm.

Received 11 July 2017; received in revised form 16 November 2017; accepted 27 December 2017AbstractOnline auctions are a popular and convenient way to engage in ecommerce. However, the amount of auction fraud has increased with the rapid surge of users participating in online auctions. Shill bidding is the most prominent type of auction fraud where a seller submits bids to inflate the price of the item without the intention of winning. Mechanisms have been proposed to detect shill bidding once an auction has finished. However, if the shill bidder is not detected during the auction, an innocent bidder can potentially be cheated by the end of the auction. Therefore, it is essential to detect and verify shill bidding in a running auction and take necessary intervention steps accordingly. This paper proposes a run-time statistical algorithm, referred to as the Live Shill Score, for detecting shill bidding in online auctions and takes appropriate actions towards the suspected shill bidders (e.g., issue a warning message, suspend the auction, etc.). The Live Shill Score algorithm also uses a Post-Filtering Process to avoid misclassification of innocent bidders. Experimental results using both simulated and commercial auction data show that our proposed algorithm can potentially detect shill bidding attempts before an auction ends.

A structure tensor based in-loop filter to remove the compression artefacts in depth video coding is proposed. Instead of involving all neighbouring depth pixels in the joint filtering process, the algorithm chooses one neighbouring depth pixel as the filter output from a reliable neighbour region which is adaptively determined by a structure tensor analysis. It can effectively reduce the negative influence of the non-similar neighbouring depth pixels and improve the quality of the synthesised virtual views. The experimental results show that the proposal is more effective in improving the depth video coding efficiency.

The conventional filter requires that all the vehicle dynamics and noise processes are completely known. As a practical fact this is usually impossible. To deal with such a problem, the adaptive constraint-filtering (ACF) method is proposed in this study. The CF method developed previously can accommodate the constraint in the filtering process for a non-linear dynamic system. However, the assumption that the modelling noise and the sensor noise are known may not be practical. Here, the fuzzy innovation adaptive estimation approach is proposed to determine the window size, which is assumed constant in the classical adaptive scheme. To assess the performance of the proposed algorithm, the Monte Carlo method is adopted. The performance of the various filters, such as the Kalman filter (KF), the adaptive KF (AKF), the CF and the adaptive CF ACF are then compared. The simulation results show that the ACF method is evidently better than the other filters. From dynamic experimental results, it is shown that the proposed methodology yields a successful algorithm to manage the ill-conditioned global positioning system positioning problem. The adaptation accuracy based on the proposed methodology is substantially improved.