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In wireless power transfer (WPT) systems, inverters are used to achieve high-frequency conversion of DC/AC, and their conversion efficiency and working frequency are key factors affecting the system's power transfer efficiency. In practical applications, many hardware issues, such as power transistor shutdown and loss, are the main reasons that affect the inverter conversion efficiency. On the other hand, the working frequency of WPT systems ranges from hundreds of kHz to a few MHz, and traditional voltage and current phasor estimation requires a very high sampling rate which is difficult to achieve. To overcome these limitations, this paper introduces a phase-shifting full bridge inverter using a zero-voltage switching (ZVS) soft switching technology to optimize the conversion efficiency of the inverter. Meanwhile, apparent power is introduced to detect the operating frequency and phase angle. Combined with an FPGA soft switching control strategy, this approach allows for the quick adjustment of the driving pulse of MOS transistors, as well as the voltage and current at the transmitting end, to a completely symmetrical state in real-time, effectively suppressing frequency offset and achieving efficient frequency tracking control and maximum efficiency tracking (MET) control of the WPT system. Through simulation and experiments, the ZVS soft switching technology has been achieved with the inverter control strategy, leading to improved conversion efficiency. The frequency offset that can be corrected can reach 0.1 Hz using the apparent power detection method, and the maximum transfer efficiency of the WPT system can reach 91%. [ABSTRACT FROM AUTHOR]

The dynamical behavior of a 5-dimensional Lorenz model (5DLM) is investigated. Bifurcation diagrams address the chaotic and periodic behaviors associated with the bifurcation parameter. The Hamilton energy and its dependence on the stability of the dynamical system are presented. The global exponential attractive set (GEAS) is estimated in different 3-dimensional projection planes. A more conservative bound for the system is determined, that can be applied in synchronization and chaos control of dynamical systems. Finally, the finite time synchronization of the 5DLM, indicating the role of the ultimate bound for each variable, is studied. Simulations illustrate the effectiveness of the achieved theoretical results. [ABSTRACT FROM AUTHOR]

Reuse and recycling of construction wastes effectively prevent further destruction of the environment and nature due to the construction industry. One of the conventional methods is the reapplication of recycled aggregates (RAs) in concrete mix design and construction. Compressive strength (CS) is a considerable representation of the mechanical properties of recycled aggregate concrete (RAC). The present study is divided into two parts of experimental and predicting. In the laboratory studies phase, RA was used with 100% replacement. Due to the proven defects in RAC, glass fibers and silica fume (variables) have been used to compensate for these defects. Different mixing designs were used to construct concrete samples, and then the CS of each specimen was measured in the laboratory. Afterward, these data were used as training and testing in various conventional, ensembled and hybrid models as the tree‐based learning algorithms, rules, Lazy‐learning Algorithms, Functions, and Meta classifiers. Considering the applied performance statistical criteria (R2, root mean squared error, mean absolute error, and mean absolute percentage error), all models have provided excellent and acceptable results, which the precision of the multilayer perceptron single model and additive regression‐random Forest hybrid model have the best agreement with measured values of CS of RAC. [ABSTRACT FROM AUTHOR]

With the application of electrical equipment, magnetically coupled resonant (MCR) wireless power transfer (WPT) technology has become an effective means to improve equipment intelligence. The MCR‐WPT system is a loosely coupled system, and the resonant frequency may be split or detuned due to the changes of load or transferring distance, resulting in the system transfer efficiency (TE) greatly reduced. To solve the problems of limited speed and accuracy in the existing frequency tracking methods, this paper analyzes the relation between the detuning rate and the system TE, proposing an adaptive frequency tracking control method based on fuzzy radial basis function neural network control. The neural network outputs proportion–integration–differentiation parameters to adjust the inverter drive circuit, and the frequency of inverter drive circuit is adjusted nonlinearly in real time to ensure the accurate frequency tracking of the MCR‐WPT system. The simulation and experimental results show that the proposed method can enhance the tracking ability of the resonant frequency, and effectively improve the system TE. [ABSTRACT FROM AUTHOR]

In this paper, we propose an enhanced pix2pix dehazing network, which generates clear images without relying on a physical scattering model. This network is a generative adversarial network (GAN) which combines multiple guided filter layers. First, the input of hazy images is smoothed to obtain high-frequency features according to different smoothing kernels of the guided filter layer. Then, these features are embedded in higher dimensions of the network and connected with the output of the generator's encoder. Finally, Visual Geometry Group (VGG) features are introduced to serve as a loss function to improve the quality of the texture information restoration and generate better hazy-free images. We conduct experiments on NYU-Depth, I-HAZE and O-HAZE datasets. The enhanced pix2pix dehazing network we propose produces increases of 1.22 dB in the Peak Signal-to-Noise Ratio (PSNR) and 0.01 in the Structural Similarity Index Metric (SSIM) compared with a second successful comparison method using the indoor test dataset. Extensive experiments demonstrate that the proposed method has good performance for image dehazing. [ABSTRACT FROM AUTHOR]

Automatic bone segmentation from a chest radiograph is an important and challenging task in medical image analysis. However, a chest radiograph contains numerous artifacts and tissue shadows, such as trachea, blood vessels, and lung veins, which limit the accuracy of traditional segmentation methods, such as thresholding and contour-related techniques. Deep learning has recently achieved excellent segmentation of some organs, such as the pancreas and the hippocampus. However, the insufficiency of annotated datasets impedes clavicle and rib segmentation from chest X-rays. We have constructed a dataset of chest X-rays with a raw chest radiograph and four annotated images showing the clavicles, anterior ribs, posterior ribs, and all bones (the complete set of ribs and clavicle). On the basis of a sufficient dataset, a multitask dense connection U-Net (MDU-Net) is proposed to address the challenge of bone segmentation from a chest radiograph. We first combine the U-Net multiscale feature fusion method, DenseNet dense connection, and multitasking mechanism to construct the proposed network referred to as MDU-Net. We then present a mask encoding mechanism that can force the network to learn the background features. Transfer learning is ultimately introduced to help the network extract sufficient features. We evaluate the proposed network by fourfold cross validation on 88 chest radiography images. The proposed method achieves the average DSC (Dice similarity coefficient) values of 93.78%, 80.95%, 89.06%, and 88.38% in clavicle segmentation, anterior rib segmentation, posterior rib segmentation, and segmentation of all bones, respectively. [ABSTRACT FROM AUTHOR]

Three polymers, namely PAMPS (P1), P(AMPS-co-NVP) (P2) and P(AMPS-co-DMC) (P3), synthesized by common solution polymerization (CSP) were investigated as corrosion inhibitors for N80 steel in 3.5 wt% NaCl flooded with CO2 by high temperature high pressure (HPHT) weight loss, electrochemical methods (EIS, Tafel), scanning electrochemical microscopy (SECM), scanning Kelvin probe (SKP), scanning vibrating electrode (SVET) techniques, and surface studies (UV-Visible, SEM). The P1 inhibitor exhibited the best inhibition efficiency among the polymers at 500 ppm concentration. The adsorption of the polymer derivatives obeyed the Langmuir isotherm. SECM measurements revealed a difference in the current on the steel surface with and without polymers. SKP and SVET showed the potential difference between the cathodic and anodic regions of the metal surface with and without inhibitors. [ABSTRACT FROM AUTHOR]

To address the instability of phylogenetic trees in morphological datasets caused by missing values, we present a phylogenetic inference method based on a concept decision tree (CDT) in conjunction with attribute reduction. First, a reliable initial phylogenetic seed tree is created using a few species with relatively complete morphological information by using biologists' prior knowledge or by applying existing tools such as MrBayes. Second, using a top-down data processing approach, we construct concept-sample templates by performing attribute reduction at each node in the initial phylogenetic seed tree. In this way, each node is turned into a decision point with multiple concept-sample templates, providing decision-making functions for grafting. Third, we apply a novel matching algorithm to evaluate the degree of similarity between the species' attributes and their concept-sample templates and to determine the location of the species in the initial phylogenetic seed tree. In this manner, the phylogenetic tree is established step by step. We apply our algorithm to several datasets and compare it with the maximum parsimony, maximum likelihood, and Bayesian inference methods using the two evaluation criteria of accuracy and stability. The experimental results indicate that as the proportion of missing data increases, the accuracy of the CDT method remains at 86.5%, outperforming all other methods and producing a reliable phylogenetic tree. [ABSTRACT FROM AUTHOR]

Background: LncRNA‐FENDRR is a kind of endothelial genes critical for vascular development. Moreover, miR‐126 and vascular endothelial growth factor A (VEGFA) are also involved in the physiological process of vascular endothelial cells. This study aimed to the underlying mechanism of FENDRR involving miR‐126 and VEGFA in hypertensive intracerebral hemorrhage (HICH). Methods: C57BL/6 mice were chosen to establish HICH model. The expression of FENDRR, miR‐126, and VEGFA at mRNA level was determined by qRT‐PCR. The protein expression of VEGFA was assessed using Western blot. RIP assay and RNA pull‐down assay were used to the relationship between FENDRR and miR‐126. Flow cytometry was used to analyze cell apoptosis. Results: The levels of FENDRR and VEGFA were increased, and miR‐126 expression was decreased in vascular endothelial cells (VECs) from the right brain of model mice and human brain microvascular endothelial cells (HBMECs) treated by thrombin. Overexpression of FENDRR promoted the apoptosis of HBMECs. FENDRR regulating VEGFA participated in HBMECs apoptosis through targeting miR‐126. Downregulation of FENDRR was indicated to relieve the HICH in mice. Conclusions: FENDRR could promote the apoptosis of HBMECs via miR‐126 regulating VEGFA in HICH. [ABSTRACT FROM AUTHOR]