Multiple Propagation Research Articles

Performance analysis of Rayleigh fading wireless networks with multiple propagation paths and spatial diversity

Performance analysis of Rayleigh fading wireless networks with multiple propagation paths and spatial diversity

Performance analysis of Rayleigh fading wireless networks with multiple propagation paths and spatial diversity

Performance analysis of Rayleigh fading wireless networks with multiple propagation paths and spatial diversity

Study on Attenuation Characteristics of Reissner's Membrane Mode in Cochlea.

Multiple propagation modes progress along the basilar membrane and Reissner's membrane, called basilar membrane mode (BM mode) and Reissner's membrane mode (RM mode), respectively. This study focuses on the effects of the RM mode on the hearing process and investigates the difference in the attenuation characteristics between the RM and BM modes in the vicinity of the cochlea base by using modal analysis. Results indicated that the RM mode has fewer effects on the hearing process, except otoacoustic emissions, due to its bigger attenuation constant than the BM mode in the vicinity of the cochlea base. The structural dependency of the attenuation constant of the RM mode is also investigated.

Identifying Multiple Propagation Sources With Motif-Based Graph Convolutional Networks for Social Networks

Identifying Multiple Propagation Sources With Motif-Based Graph Convolutional Networks for Social Networks

Data Fusion for Multipath-Based SLAM: Combining Information From Multiple Propagation Paths

Data Fusion for Multipath-Based SLAM: Combining Information From Multiple Propagation Paths

A Novel Method for Few-Shot Specific Emitter Identification in Non-Cooperative Scenarios

Obtaining larger category-label-containing training signal datasets in non-cooperative scenarios is difficult. Moreover, employing smaller labeled signal datasets for specific emitter identification is technically challenging. Therefore, we propose a novel method for few-shot SEI. We first design a bispectral analysis and Radon transformation-based signal preprocessing scheme to obtain feature vectors that effectively characterize the radio frequency fingerprints. The feature vectors are then fed to a network model for feature learning. Moreover, a meta-learning algorithm is applied to the network model to adapt to few-shot feature learning. The conventional meta-learning algorithm is improved to develop a novel algorithm involving latent embedding optimization for meta-learning. The proposed method extracts low-dimensional key features from high-dimensional input data and evaluates the distance and degree of feature dispersion. The resulting information is employed in sample point prediction. The algorithm effectively achieves few-shot SEI and offers stable and efficient recognition after training with a minimum of forty samples. This method identifies emitter individuals under multiple modulation types and exhibits scalability in identifying the emitter numbers. Moreover, it offers adaptability in identifying the emitter individuals under multiple propagation channel types.

A hybrid attention mechanism for multi-target entity relation extraction using graph neural networks

Relation extraction is a cardinal natural language processing task employed to process a given corpus and infer hidden connections and relations between real-world objects. Most contemporary research works employ state-of-the-art language models and techniques for determining the type of relationship existing between a pair of entities in a given sentence but are computationally expensive and fail to identify or match the entities present in the sentence as a single task, rather they breakdown the problem into subtasks or rely on a multi-module framework requiring multiple propagations through the network. The paper presents a novel methodology for extracting relations between multiple pairs of entities present in the sentence and performs the relation classification task. The proposed methodology employs a graph neural network, and in contrast to the existing research, the proposed mechanism makes use of a hybrid attention mechanism to dynamically optimize the graph edges to capture the relevant details in the network graph to aid as an attention mechanism to aid in faster computation, compared to the more typical transformer-based networks that overwhelm CPU-based systems. The paper also studies the effect of the number of hop transformations on the graph and other hyper-parameters controlling the input sentence representation. The proposed model architecture achieves a macro avg. F1 score of 86.2 on the SemEval 2010 relation extraction dataset, with further room for improvement.

Application of the J-Integral and Digital Image Correlation (DIC) to Determination of Multiple Crack Propagation Law of UHPC under Flexural Cyclic Loading

This study investigated the fatigue crack propagation behavior of ultra-high-performance concrete (UHPC) incorporated with different steel fiber lengths of 6, 13, and 20 mm under flexural cyclic loading, based on the Paris law and nonlinear fracture mechanics. In addition, multiple crack covering areas and fatigue J-integral amplitudes were employed to quantitatively evaluate the fatigue crack propagation rate and predicate the fatigue life of the UHPC during the steady development stage. The results indicated that the maximum crack opening displacement (COD) values were 0.312, 0.673, and 1.265 mm and the minimum crack growth rates were -3.05, -4.48 and -4.62 for SF6, SF13, and SF20, respectively. The critical crack length was approximately 65 mm for UHPC specimens containing different fiber length at a given fiber volume fraction (2.0%), indicating that the critical crack length was simply related to the fiber length. Interestingly, when the fatigue crack area of all the tested series reached approximately 35 mm2, fracture failure occurred. There were very small predictions between the actual tested and predicated fatigue lives, all less than 7.21%. Hence, it was reasonable to predict the fatigue life of the UHPC based on the J-integral according to the DIC technique.

Data transmission in a multimode optical fiber using a neural network

In digital data transmission, single mode optical fibers are commonly used since they can carry very short optical pulses without any significant distortions. In contrast, multimode fibers support many propagation modes that travel with different speeds; thus, they cannot maintain the shape of a light pulse. This feature of multiple propagation modes can be a benefit since it makes possible the transmission of data through several channels simultaneously. We demonstrate how multimode fibers can be used to transmit images. Because of the different propagation constants of the modes, the transmitted image is scrambled to apparently random speckle patterns. A simple neural network can be used to model the transmission through the multimode fiber. We show how the neural network can be trained to recognize a set of patterns with high accuracy.

BENDING FATIGUE BEHAVIOR OF AG NANOWIRE/CU THIN-FILM HYBRID INTERCONNECTS FOR WEARABLE ELECTRONICS

Enhancing the mechanical reliability of metal interconnects is important for achieving highly reliable flexible/wearable electronic devices. In this study, Ag nanowire and Cu thin-film hybrid interconnects were explored as a novel concept to enhance mechanical reliability under bending fatigue. Bending fatigue tests were conducted on the Cu thin films and Cu/Ag nanowire/polyimide (CAP) interconnects. The increase in resistance was larger for the Cu thin films than for the CAP. The single-component Cu electrodes showed multiple crack initiation and propagation due to bending strain, which degraded the electrical conductivity. In CAP, however, no long-range cracks were observed, even after 300,000 cycles of bending, although a wavy structure was observed, probably due to the delamination of the Ag nanowires under repeated bending. Our study confirms that flexible Ag nanowire and metal thin-film hybrids can enhance the mechanical reliability of metal thin-film interconnects under bending fatigue.

The Effects of Meridian Surface Shape on the Pressure Pulsation of a Multi-Stage Electric Submersible Pump

The influence mechanism of the internal pressure fluctuation propagation law of multi-stage submersible electric pump (ESP) is still unclear, which has been a major problem restricting the stable exploitation of deep-sea oil and gas. In order to investigate the effect of different meridian profiles on the pressure pulsation characteristics of three-stage submersible electric pumps, the unsteady Reynolds-averaged Navier–Stokes (URANS) method is used to numerically investigate it. The results show that the lower the pressure pulsation amplitude in the pump caused by the meridional shape that is more in line with the flow law, has a positive effect on the operation stability. The change of the shape of the meridian greatly affects the pressure pulsation law in the secondary and final pumps. The rotor–stator interaction causes the pressure pulsation amplitude of the monitoring point in the middle of the pump chamber to reach a peak value. By using continuous wavelet transform analysis, it is found that the regularity of 1–2 times frequency conversion is complicated due to multiple pulsation sources and low frequency propagation coupling between stages. At 3–6 times frequency, it is basically close to the pulsation rule of the blade frequency. The above research provides a basis for improving the operation stability of the ESP.

Adaptive multi-feature budgeted profit maximization in social networks

Online social network has been one of the most important platforms for viral marketing. Most of existing researches about diffusion of adoptions of new products on networks are about one diffusion. That is, only one piece of information about the product is spread on the network. However, in fact, one product may have multiple features and the information about different features may spread independently in social network. When a user would like to purchase the product, he would consider all of the features of the product comprehensively not just consider one. Based on this, we propose a novel problem, multi-feature budgeted profit maximization (MBPM) problem, which first considers budgeted profit maximization under multiple features propagation of one product. Given a social network with each node having an activation cost and a profit, MBPM problem seeks for a seed set with expected cost no more than the budget to make the total expected profit as large as possible. We mainly consider MBPM problem under the adaptive setting, where seeds are chosen iteratively and next seed is selected according to current diffusion results. We study adaptive MBPM problem under two models, oracle model and noise model. The oracle model assumes conditional expected marginal profit of any node could be obtained in O(1) time, and a \((1-1/e)\) expected approximation policy is proposed. Under the noise model, we estimate conditional expected marginal profit of a node by modifying the EPIC algorithm and propose an efficient policy, which could achieve a \((1-e^{-(1-\epsilon )})\) expected approximation ratio. Several experiments are conducted on six realistic datasets to compare our proposed policies with their corresponding non-adaptive algorithms and some heuristic adaptive policies. Experimental results show efficiencies and superiorities of our policies.

Learning From Crowds With Multiple Noisy Label Distribution Propagation.

Crowdsourcing services provide a fast, efficient, and cost-effective way to obtain large labeled data for supervised learning. Unfortunately, the quality of crowdsourced labels cannot satisfy the standards of practical applications. Ground-truth inference, simply called label integration, designs proper aggregation methods to infer the unknown true label of each instance (sample) from the multiple noisy label set provided by ordinary crowd labelers (workers). However, nearly all existing label integration methods focus solely on the multiple noisy label set per individual instance while totally ignoring the intercorrelation among multiple noisy label sets of different instances. To solve this problem, a multiple noisy label distribution propagation (MNLDP) method is proposed in this article. MNLDP at first estimates the multiple noisy label distribution of each instance from its multiple noisy label set and then propagates its multiple noisy label distribution to its nearest neighbors. Consequently, each instance absorbs a fraction of the multiple noisy label distributions from its nearest neighbors and yet simultaneously maintains a fraction of its own original multiple noisy label distribution. Empirical studies on a collection of an artificial dataset, six simulated UCI datasets, and three real-world crowdsourced datasets show that MNLDP outperforms all other existing state-of-the-art label integration methods in terms of the integration accuracy and classification accuracy.

Numerical simulation on the multiple planar fracture propagation with perforation plugging in horizontal wells

Intra-stage multi-cluster temporary plugging and diverting fracturing (ITPF) is one of the fastest-growing techniques to obtain uniform reservoir stimulation in shale gas reservoirs. However, propagation geometries of multiple fractures during ITPF are not clear due that the existing numerical models cannot capture the effects of perforation plugging. In this paper, a new three-dimensional FEM based on CZM was developed to investigate multiple planar fracture propagation considering perforation plugging during ITPF. Meanwhile, the fluid pipe element and its subroutine were first developed to realize the flux partitioning before or after perforation plugging. The results showed that the perforation plugging changed the original distribution of the number of perforations in each fracture, thus changing the flux partitioning after perforation plugging, which could eliminate the effect of stress interference between multiple fractures and promote a uniform fluid distribution. The standard deviation of fluid distribution in the perforation plugging case was only 8.48% of that in the non-diversion case. Furthermore, critical plugging parameters have been investigated quantitatively. Specifically, injecting more diverters will create a higher fluid pressure rise in the wellbore, which will increase the risk of wellbore integrity. Comprehensively considering pressure rise and fluid distribution, the number of diverters should be 50% of the total number of perforations ( N pt ), whose standard deviation of fluid distribution of multiple fractures was lower than those in the cases of injecting 10% N pt , 30% N pt and 70% N pt . The diverters should be injected at an appropriate timing, i.e. 40% or 50% of the total fracturing time ( t ft ), whose standard deviation of the fluid distribution was only about 20% of standard deviations in the cases of injecting at 20% t ft or 70% t ft . A single injection with all diverters can maintain high bottom-hole pressure for a longer period and promote a more uniform fluid distribution. The standard deviation of the fluid distribution in the case of a single injection was 43.62%–55.41% of the other cases with multiple injection times. This study provides a meaningful perspective and some optimal plugging parameters on the field design during IPTF.

A Graph-Neural-Network-Based Social Network Recommendation Algorithm Using High-Order Neighbor Information.

Social-network-based recommendation algorithms leverage rich social network information to alleviate the problem of data sparsity and boost the recommendation performance. However, traditional social-network-based recommendation algorithms ignore high-order collaborative signals or only consider the first-order collaborative signal when learning users’ and items’ latent representations, resulting in suboptimal recommendation performance. In this paper, we propose a graph neural network (GNN)-based social recommendation model that utilizes the GNN framework to capture high-order collaborative signals in the process of learning the latent representations of users and items. Specifically, we formulate the representations of entities, i.e., users and items, by stacking multiple embedding propagation layers to recursively aggregate multi-hop neighborhood information on both the user–item interaction graph and the social network graph. Hence, the collaborative signals hidden in both the user–item interaction graph and the social network graph are explicitly injected into the final representations of entities. Moreover, we ease the training process of the proposed GNN-based social recommendation model and alleviate overfitting by adopting a lightweight GNN framework that only retains the neighborhood aggregation component and abandons the feature transformation and nonlinear activation components. The experimental results on two real-world datasets show that our proposed GNN-based social recommendation method outperforms the state-of-the-art recommendation algorithms.

Predicting mixed mode damage propagation in snowpack using the extended cohesive damage element method

A novel extended cohesive damage element method is used to develop a numerical snowpack model to study the fundamental damage mechanisms of snowpack under external drivers and to investigate multiple mixed mode damage propagation within snowpack. A new mixed mode damage criterion is introduced to account for tensile and shear fractures as well as compressive crushing together with shear crack for approximating the mixed mode damage initiation and propagation in the weak layer in snowpack. A propagation saw test (PST) is considered to understand basic damage involution in snowpack under self-weight related bending. The nonlinear fracture modelling prediction agrees with the PST sample well. This paper provides an alternative approach as a predictive method using the extended cohesive damage element for potentially forecasting slab avalanches in snow terrain according to weather forecast and planned human activities in the future.

Experimental Study on the Shear Behavior of Reinforced Highly Ductile Fiber-Reinforced Concrete Beams with Stirrups

The aim of this study is to improve the shear behavior of reinforced concrete (RC) beams with stirrups by using highly ductile fiber-reinforced concrete (HDC), which is a fiber-reinforced cement-based composite material with tensile-strain-hardening properties. Twelve reinforced HDC (RHDC) beams and three RC beams with stirrups were tested under a concentrated load. The experimental parameters involved the shear span to effective depth ratio, stirrup ratio, and longitudinal reinforcement ratio. The results revealed that the mode of failure of RHDC beams, which exhibited better ductility than RC beams, included diagonal compression, shear compression, diagonal tension, and flexural shear failure. RHDC beams exhibited stable multiple crack propagation behavior and satisfactory integrity, thus showing that HDC effectively restricted the development of shear cracks and improved the damage resistance of beams. Compared with RC beams, the shear strength, displacement ductility factor, and deflection-clear span ratios corresponding to the peak load and ultimate deflection increased by up to 30.5%, 44.9%, 150.0%, and 148.0%, respectively. RHDC beams exhibited higher residual strength and deformation capacity than RC beams, thus indicating that HDC significantly improved the brittle shear failure mode. Specimens H-1 and H-2 exhibited the largest improvement in shear strength and displacement ductility factor, respectively, compared with RC beams. The shear strength of RHDC beams increased as the shear span to effective depth ratio decreased. For RHDC beams with the same shear span to effective depth ratio, the shear strength increased with the increase in the longitudinal reinforcement ratio and stirrup ratio under shear compression failure.

Evolution of creep rupture mechanism in advanced powder metallurgy superalloys with tantalum addition

To further understand the effect of Ta additive on the creep damage for polycrystalline Ni-base superalloys, creep rupture tests on powder metallurgy (PM) superalloys with various Ta were carried out under different conditions in this study. The results indicated that the multiple crack propagation and damage mechanisms could occur at the identical creep condition. These different fracture mode areas had distinct boundaries on the fracture surface, its size could be altered by Ta addition. However, the effects of Ta were radically changed by the creep conditions. The creep rupture mechanism transformed significantly with temperatures and applied stresses, and even subversive laws appeared in the creep life for high-Ta alloys. The substructure observation after creep rupture demonstrated that the annealing coherent twin boundaries gradually lose coherence with the matrix, which could become sites for cavities nucleation or crack propagation that followed. Furthermore, the creep-induced microtwins were critical for the creep damage and rupture. The electron back-scattered diffraction (EBSD), high-resolution transmission electron microscope (TEM) imaging, and quantitative elemental analysis assisted in comprehending these alterable creep rupture mechanisms.

Experimental study on the multiple fracture simultaneous propagation during extremely limited-entry fracturing

Horizontal well with multi-stage fracturing is one of the most effective stimulation methods for unconventional reservoirs, e.g. tight oil/gas or shale. To maximize reservoir stimulation volume (SRV), tighter fracture spacing and fewer perforations are distributed in one stage during extreme limited-entry fracturing (XLEF) in recent years. However, the fracture geometries and injection pressure curve are not clear when multiple fractures with close spacing were created simultaneously in the perforated wellbore during XLEF. This study investigated the multiple fracture simultaneous propagation in the XLEF perforated wellbore based on the true tri-axial fracturing experiments. Critical factors of horizontal stress difference (HSD), the number of perforation clusters, helical/in-plane perforated method, number of perforations per cluster and fracturing fluid flowrate were investigated in detail. The results showed that, firstly, compared to one fracture produced by the helical perforated method, XLEF with the in-plane perforated method has a higher breakdown pressure and could simultaneously create multiple transverse fractures. Secondly, longitudinal fractures and a small number of curved transverse fractures occurred simultaneously under lower HSD conditions, while multiple parallel transverse fractures could be created under high HSD conditions. Thirdly, increasing the number of perforations per cluster will reduce perforation cluster effectiveness, and increasing the number of clusters will lead to the merging of multiple fractures. Finally, three relationships between pressure response and fracture geometries during XLEF, e.g. single transverse fracture, multiple transverse fractures, co-existence of longitudinal and transverse fractures, have been revealed. This study provides a meaningful perspective for the multiple fracture propagation in the perforated wellbore, which could help the field fracturing design during XLEF.

Parametric effects on fracture geometries from multi-fracture propagation emanating from neighbouring wellbores in quasi-brittle rocks

An integrated approach is presented using field data input from measured geological information into numerical simulation for understanding effects of induced stress on geometries of multiple fracture propagation. We establish a benchmark study based on comparison of field result with numerical computations. The comparison then acts as reference measures for studying effects of changing in-situ stress, fracturing fluid viscosity and fracture spacing on propagation and geometries of multiple fractures between neighbouring wellbores in an undisclosed gas field. This leverages understanding of more complexities associated with inter-well multiple fracture growth that are idealized as straight from certain perspectives. Although some studies focus on stress interference from pre-existing fractures, actual fracture propagation geometries may be far from the idealized scenarios. Therefore, the stress shadow effects between growing multiple hydraulic fractures, if not taken into account, can lead to unrealistic estimation of hydraulic fracture trajectories. Consequently, more attention should be paid to the actual propagation of hydraulic fractures. Actual field geologic information is provided through well-logging and field mapping data. Very short fractures were propagated in these wells before the operation was terminated due to technical problems. The reservoir depth in the area is about 2170 m. At such depth, quasi-brittleness of shale should be accounted for by using relevant methods that capture rock ductility such as traction separation law. Abaqus commercial software is used to conduct the numerical computation using extended finite element method based on cohesive zone modeling. Application of this technique is further validated using Kristianovich-Geertsma-de Klerk analytical solution. This study is important in field implementation of infill drilling with well-informed mechanism of fracture interference in the inter-well region.