Search Results (8)

Power quality measurements are essential to monitor, analyse and control the operation of smart grids within power systems. This work aims to develop and calibrate a PQ network analyser. As the penetration of non-linear loads connected to power systems is increasing every day, it is essential to measure power quality. In this sense, a power quality (PQ) analyser is based on the high-speed sampling of electrical signals in single-phase and three-phase electrical installations, which are available in real time for analysis using wireless Wi-Fi (Wireless-Fidelity) networks. The PQAE (Power Quality Analyser Embedded) power quality analyser has met the calibration standards for Class A devices from IEC 61000-4-30, IEC 61000-4-7 and IEC 62586-2. In this paper, a complete guide to the tests included in this standard has been provided. The Fast Fourier Transform (FFT) obtains the harmonic components from the measured signals and the window functions used reduce spectral leakage. The window size depends on the fundamental frequency of, intensity of and changes in the signal. Harmonic measurements from the 2nd to 50th harmonics for each phase of the voltage and each phase and neutral of the current have been performed, using the Fast Fourier transform algorithm with various window functions and their comparisons. PQAE is developed on an open-source platform that allows you to adapt its programming to the measurement needs of the users. Full article

This research initiative, developed in response to the need for enhanced mechanization efficiency within solar greenhouses, particularly under yellow sand cultivation conditions, introduces an integrated ditching and film-covering machine. A novel spiral staggered throw-cut combined ditching knife was specifically engineered and optimized to meet the exacting agronomic requirements of embedded substrate cultivation. Extensive analyses of soil interactions and the formulation of dynamic equations for soil particles facilitated the determination of key operational parameters: a tangent height of 650 mm for the ditching knife, a soil-throwing width of 300 mm, a piece width of 120 mm, and an inclination angle of 30°. Performance simulations of the ditching knife, conducted using the discrete element method (DEM), revealed superior soil disturbance control and improved soil return compared to conventional designs. Critical operational variables such as forward speed, knife shaft speed, and ditching depth were rigorously tested, with trench depth quality and power consumption as primary evaluation metrics. The results demonstrated that knife shaft speed profoundly influences performance, with optimal operating parameters established through detailed field testing: a speed of 0.5 m/s, a blade shaft speed of 200 rpm, and a ditching depth of 300 mm. Under these optimized conditions, the machine achieved power consumption of 0.668 kW, trench depth stability of 86.7%, a surface width of 413 mm, a bottom width of 304 mm, and an average ditching depth of 310 mm, achieving a qualification rate of 87.1%. The post-ditching soil crushing rate was 92.4%. Both simulation and field evaluations validated that the innovative ditching knife markedly enhances ditching and soil-throwing quality in sandy soil, fulfills agronomic requirements for tomato sowing, and provides an essential reference for the mechanized planting of crops in the yellow sand matrix cultivation mode of solar greenhouses. Full article

Tower fatigue and strength are crucial operational concerns of floating offshore wind turbines (FOWTs) due to the escalation of the vibration phenomena observed on these structures as compared to land-based ones. FOWT towers are excited by wave and wind polyperiodic disturbances yielding continual transient states of structural vibration that are challenging for vibration mitigation systems. Thus, the paper investigates a novel implementation of nonlinear optimal-based vibration control solutions for the full-scale, tension leg platform (TLP)-based, NREL 5MW wind turbine tower-nacelle model with a 10-ton tuned vibration absorber (TVA), equipped with a magnetorheological (MR) damper, located at the nacelle. The structure is subjected to excessive wave and wind excitations, considering floating platform motions derived from model experiments in a wave tank. The MR damper operates simultaneously with an electromagnetic force actuator (forming a hybrid TVA) or independently (a semiactive TVA). The study includes both actuators’ nonlinearities and dynamics, whereby the former are embedded in the Hamilton-principle-based nonlinear control solutions. The TVA is tuned either to the NREL 5MW tower-nacelle 1st bending mode frequency (TVA-TN) or to the TLP surge frequency (TVA-TLP). The optimal control task was redeveloped concerning the TVA stroke and transient vibration minimisation, including the implementation of the protected structure’s acceleration and relative displacement terms, as well as the nonzero velocity term in the quality index. The regarded model is embedded in a MATLAB/Simulink environment. On the basis of the obtained results, the TVA-TN solution is by far superior to the TVA-TLP one. All the regarded TVA-TN solutions provide a tower deflection safety factor of ca. 2, while reference systems without any vibration reduction solutions or with a passive TVA-TLP are at risk of tower structural failure as well as the hybrid TVA-TLP system. The obtained TVA stroke reductions of 25.7%/22.0% coincide with 3.6%/10.3% maximum tower deflection reductions for the semiactive/hybrid TVA-TN case (respectively) with regard to the previously developed approaches. Moreover, these reductions are obtained due to the sole control algorithm enhancement; thus, no additional resources are necessary, while this attainment is accompanied by a reduction in the required MR damper force. The lowest obtained TVA stroke amplitude of 1.66 m is guaranteed by the newly introduced semiactive control. Its hybrid equivalent ensures 8% lower primary structure deflection amplitude and reduced nacelle acceleration levels thanks to the utilisation of the force actuator of the relatively low power (ca. 6 kW); the trade-off is an increased TVA stroke amplitude of 2.19 m, which, however, is the lowest among all the tested hybrid solutions. The analysed reference passive TVA systems, along with a modified ground-hook hybrid solution, can hardly be implemented in the nacelle (especially along the demanding side–side direction). The latter, being the well-proven hybrid solution for steady-state tower deflection minimisation, yielded unsatisfactory results. The achievements of the study may be used for an effective design of a full-scale vibration reduction system for the TLP-based floating wind turbine structure. Full article

In this paper, we investigate the performance of covert communications in different types of a relay system: decode-and-forward (DF), compress-and-forward (CF) and amplify-and-forward (AF). We consider a source node that attempts to send both public and covert messages to a destination node through a relay on which a covert message detector is embedded. By taking the minimum detection error probability (DEP) at the relay into account, we optimize the power distribution between the public and covert messages to achieve the maximum covert rate. We further make a delay-aware comparison among DF, CF and AF relay systems with the obtained closed-form covert rates and conduct an extensive examination on the asymptotic behaviors in different limits. Our analyses reveal that CF or AF tend to outperform DF for high source transmit power or low relay transmit power, while various system parameters such as the processing delay, minimum required quality of service for public messages and DEP threshold lead to different performance relationships among DF, CF and AF for high relay transmit power. Numerical results verify our investigation into the performance comparison in various channel models. Full article

In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition. Various image-denoising approaches, including median, Gaussian, and bilateral filters, are available in the literature. Since convolutional neural networks (CNN) are able to directly learn complex patterns and features from data, they have become a popular choice for image-denoising tasks. As a result of their ability to learn and adapt to various denoising scenarios, CNNs are powerful tools for image denoising. Some deep learning techniques such as CNN incorporate denoising strategies directly into the CNN model layers. A primary limitation of these methods is their necessity to resize images to a consistent size. This resizing can result in a loss of vital image details, which might compromise CNN’s effectiveness. Because of this issue, we utilize a traditional denoising method as a preliminary step for noise reduction before applying CNN. To our knowledge, a comparative performance study of CNN using traditional and embedded denoising against a baseline approach (without denoising) is yet to be performed. To analyze the impact of denoising on the CNN performance, in this paper, firstly, we filter the noise from the images using traditional means of denoising method before their use in the CNN model. Secondly, we embed a denoising layer in the CNN model. To validate the performance of image denoising, we performed extensive experiments for both traffic sign and object recognition datasets. To decide whether denoising will be adopted and to decide on the type of filter to be used, we also present an approach exploiting the peak-signal-to-noise-ratio (PSNRs) distribution of images. Both CNN accuracy and PSNRs distribution are used to evaluate the effectiveness of the denoising approaches. As expected, the results vary with the type of filter, impact, and dataset used in both traditional and embedded denoising approaches. However, traditional denoising shows better accuracy, while embedded denoising shows lower computational time for most of the cases. Overall, this comparative study gives insights into whether denoising will be adopted in various CNN-based image analyses, including autonomous driving, animal detection, and facial recognition. Full article

The paper discusses the possibilities of using particle filter estimation algorithms in embedded systems. For this purpose, the dedicated testing platform was built, which allowed for the determination of the estimation quality of a particle filter on a real system, and the microcontroller performance in that scenario. Tests were performed using the obsolete and not very efficient, although energy-saving, STM32F4 Discovery board—it has allowed for an in-depth analysis, and the results can be easily improved by switching to a modern platform. The quality of operations in open- and closed-loop systems was investigated based on the analysis of time simulations conducted for various mathematical models. These analyses made it possible to establish a correlation between the number of particles and the required calculation power. They have shown that it is possible to successfully implement and run a particle filter algorithm on an older and computationally limited device, as well as in real-time scenarios. Full article

Nowadays, we can observe the applications of machine learning in every field, ranging from the quality testing of materials to the building of powerful computer vision tools. One such recent application is the recommendation system, which is a method that suggests products to users based on their preferences. In this paper, our focus is on a specific recommendation system called movie recommendation. Here, we make use of user reviews of movies in order to establish a general outlook about the movie and then use that outlook to recommend that movie to other users. However, a huge number of available reviews has baffled sophisticated review systems. Consequently, there is a need to find a method of extracting meaningful information from the available reviews and use that in classifying a movie review and predicting the sentiment in each one. In a typical scenario, a review can either be positive, negative, or indifferent about a movie. However, the available research articles in the field mainly consider this as a two-class classification problem—positive and negative. The most popular work in this field was performed on Stanford and Rotten Tomatoes datasets, which are somewhat outdated. Our work is based on self-scraped reviews from the IMDB website, and we have annotated the reviews into one of the three classes—positive, negative, and neutral. Our dataset is called JUMRv1—Jadavpur University Movie Recommendation dataset version 1. For the evaluation of JUMRv1, we took an exhaustive approach by testing various combinations of word embeddings, feature selection methods, and classifiers. We also analysed the performance trends, if there were any, and attempted to explain them. Our work sets a benchmark for movie recommendation systems that is based on the newly developed dataset using a three-class sentiment classification. Full article

Liquid-based cytology (LBC) analysis of sputum is a useful diagnostic and prognostic tool for detecting lung cancer. DNA and RNA derived from lung cancer cells can be used for this diagnosis. However, the quality of cytological material is not always adequate for molecular analysis due to the effect of formalin in the commercially available fixation kits. In this study, we examined DNA and RNA extraction methods for LBC analysis with formalin fixation, using lung carcinoma cell lines and sputum. The human non-small cell lung cancer cell lines were fixed with LBC fixation reagents, such as CytoRich red preservative. Quantification of thyroid transcription factor-1 (TTF-1) and actin mRNA, epidermal growth factor receptor (EGFR) DNA in HCC827, H1975, and H1299 cells, and mutation analysis of EGFR in HCC827 and H1975 cells were performed by quantitative PCR (qPCR) and fluorescence resonance energy transfer (FRET)-based preferential homoduplex formation assay (F-PHFA) method, respectively. mRNA and DNA extracted from cell lines using RNA and/or DNA extraction kits for formalin-fixed paraffin-embedded (FFPE) fixed with various LBC solutions were efficiently detected by qPCR. The detection limit of EGFR mutations was at a rate of 5% mutated positive cells in LBC. The detection limit of the EGFR exon 19 deletion in HCC827 was detected in more than 1.5% of the positive cells in sputum. In contrast, the detection limit of the T790M/L858R mutation in H1975 was detected in more than 13% of the positive cells. We also detected EGFR mutations using next generation sequencing (NGS). The detection limit of NGS for EGFR mutation was lower than that of the F-PHFA method. Furthermore, more than 0.1% of positive cells could be cytomorphologically detected. Our results demonstrate that LBC systems are powerful tools for cytopathological and genetic analyses. However, careful attention should be paid to the incidence of false negative results in the genetic analysis of EGFR mutations detected by LBC. Full article