Random Directions Research Articles

Quasi-real dataset generation and network improvement in defect detection based on shearography

Shearography combined with deep learning is increasingly becoming a commonly used approach in non-destructive testing (NDT). However, the validity of deep learning depends on the reliability of the training dataset, and the acquisition of real datasets is costly. In this study, the use of a shearography dataset that contains real noise-generating method is proposed. This dataset can generate random shearing direction and shearing amount defects only after one electronic speckle pattern interference experiment and annotation. Moreover, this study improves the existing semantic segmentation network and identify the defect location for the defect phase map containing noise accurately. Real NDT experiments using composite materials validate the effectiveness of the dataset generation method and semantic segmentation network proposed in this study.

EFFEK PERLAKUAN ALKALI NATRIUM HIDROKSIDA TERHADAP KARAKTER FISIS DAN MEKANIS KOMPOSIT SERAT NANAS DENGAN PENGUAT RESIN POLIESTER

The use of pineapple fiber is still not optimal and has the potential to be used as a reinforcing material for hybrid composite materials which are expected to get higher composite material strength values. The aim of the research was to determine the dampening effect of NaOH solution on the mechanical and physical properties of pineapple fibrous composites. The research was conducted during the day at the Muhammadiyah University of Semarang (UNIMUS). The research stage is making test samples with molds according to ASTM D638 and ASTM D790 standards by pouring using Yukalac BQTN Polyester Resin. The resulting mold is then mixed with a catalyst and the pineapple fiber is applied in random directions. Tests are carried out mechanically, namely tensile and bending. The test results were then taken with macro photos. The NaOH solution concentration was given starting from 0%, 5%, 10%, and 15% with soaking for 24 hours. The results of the research were that the tensile test produced the largest tensile strength value for the 10% concentration specimen at 24.20 Mpa, while the largest elastic modulus value at the 5% concentration was 415.467 Mpa. In the flexural test, the highest value was produced for the 5% concentration specimen at 1.09 Mpa and the highest elastic modulus value at the 15% concentration was 16,350 Mpa.

Sound transmission reduction of double walls using porous and viscoelastic materials

Double-walled structures separated by an acoustic cavity are widely used in many applications for sound insulation purposes. Their sound transmission loss is estimated by calculating the ratio between the incident sound power on the first wall and the radiated sound power by the second. The introduction of a melamine foam associated with heavy masses brings a better sound insulation against external noise. In this context, this work deals with the numerical prediction of the sound transmission of a roller shutter box equipped with porous and viscoelastic materials. Such system can be modeled, at first approximation, as a double-walled structure. The proposed approach is based on the use of the finite element method for the modeling of the different solid and fluid domains of the problem. A mixed displacement pressure formulation of the Biot poroelasticity equations is used to model the poroelastic domain. The structure is excited by a diffuse field represented by a superposition of plane waves with random phases and directions. The acoustic power radiated by the second wall is calculated by applying the Rayleigh integral method. The results show the importance of optimizing the position of the porous and the heavy layers to maximize the sound insulation.

Numerical and experimental investigation of sound transmission through roller shutter boxes

Roller shutter boxes are essential components of building facades, providing crucial thermal and acoustic insulation. Conventional laboratory tests have been traditionally used to evaluate the sound transmission of roller shutter boxes. However, these tests can be expensive and lack repeatability and reproducibility, especially in the low-frequency range. To overcome these limitations, this study aims to introduce an advanced numerical model capable of accurately predicting the vibroacoustic response of roller shutter boxes and optimizing their acoustic insulation performance. The proposed numerical approach utilizes the finite element method to model the various solid and fluid domains within the roller shutter box structure. To capture the behavior of the poroelastic layers, a mixed displacement–pressure formulation of the Biot poroelasticity equations is employed. Excitation of the structure is achieved using a diffuse field comprising a superposition of plane waves with random phases and directions, while the resulting sound radiation is computed utilizing the infinite elements method. To validate the proposed numerical model, a comparison is made with results obtained from laboratory tests, and the experimental protocol is thoroughly described. The practical application of the proposed numerical method extends to the investigation of several influential factors on the acoustic behavior of roller shutter boxes, including assembly conditions, the positioning of poroelastic layers, and the inclusion of heavy masses.

Association between sports expertise and visual attention in male and female soccer players.

Visual attention plays a crucial role in daily living and in sports, affecting an athlete's performance and thus, potentially, the outcome of a match. However, studies assessing the association between the level of sports expertise and visual attention have yielded mixed results. This study was conducted to examine whether visual attention could be developed with increased sports expertise, and whether visual attention differed between male athletes and female athletes. A total of 128 participants were included in this study: 64 first-level national soccer athletes recruited from college soccer teams (considered elite athletes; 32 men and 32 women with similar soccer performance requirements and training experience), and 64 physical education college students with limited soccer experience (considered novice athletes; 32 men and 32 women with matched soccer experience). To assess visual attention, we used a multiple object tracking (MOT) task with four targets among a total of 10 objects moving at a fixed speed of 10°/s in random directions across a computer monitor screen. Tracking accuracy on the MOT task was calculated for each participant as the proportion of correctly selected targets. A univariate analysis of variance was performed, with group (expert, novice) and sex (male, female) as independent variables, and tracking accuracy on the MOT task as the dependent variable to assess whether sports expertise or sex influenced visual attention. Simple effects tests followed by comparisons with Bonferroni corrections were used, and effect size calculations were performed using Cohen's f statistic. Tracking accuracy on the MOT task was significantly affected by sports expertise (F(1,124) = 91.732, p < 0.001, ηP2 = 0.425), with accuracy among expert soccer athletes superior to that among novice soccer athletes. Moreover, a statistically significant interaction between sports expertise and sex was detected (F(1,124) = 7.046, p = 0.009, η P2= 0.054). Better tracking performance was observed for male soccer players (mean [SD], 0.39 [0.12]) than for female soccer players (mean [SD], 0.27 [0.08]); p<0.01; d=1.17; r=0.51) but only in the novice group. No significant sex difference was detected in tracking performance between elite male soccer athletes (mean [SD], 0.51 [0.09]) and elite female soccer athletes (mean [SD], 0.49 [0.11]). These findings confirm previous results indicating that long-term extensive sports training develops visual attention as assessed by MOT performance and extend previous findings to include soccer athletes. The findings of a sex difference in visual attention among novice soccer players but not among elite soccer athletes who had similar performance requirements and training experience suggest that long-term extensive training may minimize the sex difference in visual attention.

Explicit effort may not influence perceptuomotor decision-making.

Many decisions that humans make are enacted by the action system. For example, humans use reach-to-grasp movements when making perceptuomotor decisions between and obtaining fruits of varying quality from a pile. Recent work suggests that the characteristics of each action alternative may influence the decision itself-there may be a bias away from making perceptuomotor alternatives associated with high effort when participants are unaware of the effort differences between responses. The present study examined if perceptuomotor decisions were influenced by explicit reaching effort differences. Neurotypical human participants were presented with random dot motion stimuli in which most dots moved in random directions and varying percentages of remaining dots moved coherently left- or rightward. Participants reported leftward motion judgements by performing leftward (or left hand) reaching movements and rightward motion judgements by performing rightward (or right hand) reaching movements. A resistance band was affixed to participants' wrists and to the table in different configurations. The configurations allowed for one movement/motion direction judgement to always require stretching of the band and, therefore, require relatively more effort. Across a set of experiments, the response context (i.e. selecting directions within a limb or selecting between limbs) and the effort difference between responses were manipulated. Overall, no experiment revealed a bias away from the perceptuomotor decision associated with high effort. Based on these results, it is concluded that, in this biomechanical context, explicit effort may not influence perceptuomotor decision-making and may point to a contextual influence of action effort on perceptuomotor decision-making.

Energy and Mobility Models based Performance Evaluation in MANET

Mobile ad hoc networks are constituted with randomly moving nodes and movement of these nodes is depended upon moving model used in the network. Performance of the network directly depends on the movements and energy consumed in a specific time period by the nodes. Also performance of the protocol used for communication depends on the type of mobility model used by that specific protocol. In this paper, performance of AODV (Ad hoc On demand Distance Vector) routing protocol have been evaluated in respect of five mobility models Random Way Point Mobility Model, Manhattan Grid Mobility Model, Gauss Markov Mobility Model, Random Direction Mobility Model, RPGM (Reference Point Group Mobility)). Performance metrics are considered as: average energy consumption and average residual energy. By varying the network connections, speed of the nodes, and node densities, in different scenarios, routing protocol has been simulated in network simulator 2. Simulation results show that reference point group mobility model is best suitable model as compared to other mobility models for AODV protocol in terms of energy consumption.

Research on Mechanical Characteristics and Load-bearing Behavior of Nodes of Steel Fiber Concrete Arch Bridges

Steel fiber reinforced concrete (SFRC) is a new type of composite material formed by dispersing discontinuous short steel fibers in a uniform and random direction throughout the concrete, which has excellent properties of tensile, shear and high toughness, can effectively improve the load-bearing capacity of arch bridges. In this paper, the mechanics of SFRP in the nodal structure of an arch bridge is initially explored by means of indoor model tests and numerical simulation studies. The results show that the maximum principal tensile stress occurs at the intersection of arch rib, arch foot and tie beam, and the maximum principal compressive stress occurs at the connection between arch rib and arch seat, which should be considered in the design. In this paper, we hope to understand the characteristics of the force and bearing capacity of this type of structure to provide a reference for similar projects.

Real-time AI-based inference of people gender and age in highly crowded environments

Gender identity is one of the most fundamental aspects of life. Automatic gender identification is increasingly being used in areas such as security, marketing, and social robots. The objective of this paper is to address the challenges of gender and age identification in very crowded/noisy environments where faces are unclear and/or people are moving in relatively random directions. It presents an end-to-end real-time intelligent video analytics solution for instant people counting, gender and age estimation in crowded and open environments. The proposed solution includes a complete pipeline for training vision deep learning models and deploying them to edge devices connected to a distributed streaming analytics server. Our final Deep Learning architecture is an extended version of FairMOT, a multi-object tracking model, with two additional layers for multi-class gender classification and age regression. The training phase is performed using an enhanced and enriched version of the CrowdHuman dataset, a public dataset for human detection, with gender and age annotations added. The overall system has been validated for various movies and has shown state-of-the-art performance in terms of people tracking, gender and age inference. Our code, models, and data can be found at https://github.com/jasseur2017/people_gender_age.

Azimuthal rotation induced by the Marangoni force makes small Leidenfrost droplets move in random zigzag directions

Azimuthal rotation induced by the Marangoni force makes small Leidenfrost droplets move in random zigzag directions

A Lévy Flight-Inspired Random Walk Algorithm for Continuous Fitness Landscape Analysis

Heuristic algorithms are effective methods for solving complex optimization problems. The optimal algorithm selection for a specific optimization problem is a challenging task. Fitness landscape analysis (FLA) is used to understand the optimization problem's characteristics and help select the optimal algorithm. A random walk algorithm is an essential technique for FLA in continuous search space. However, most currently proposed random walk algorithms suffer from unbalanced sampling points. This article proposes a Lévy flight-based random walk (LRW) algorithm to address this problem. The Lévy flight is used to generate the proposed random walk algorithm's variable step size and direction. Some tests show that the proposed LRW algorithm performs better in the uniformity of sampling points. Besides, the authors analyze the fitness landscape of the CEC2017 benchmark functions using the proposed LRW algorithm. The experimental results indicate that the proposed LRW algorithm can better obtain the structural features of the landscape and has better stability than several other RW algorithms.

Deep Learning-Based Subsurface Damage Localization Using Full-Field Surface Strains.

Structures in their service life are often damaged as a result of aging or extreme events such as earthquakes or storms. It is essential to detect damage in a timely fashion to ensure the safe operation of the structure. If left unchecked, subsurface damage (SSD) can cause significant internal damage and may result in premature structural failure. In this study, a Convolutional Neural Network (CNN) has been developed for SSD detection using surface strain measurements. The adopted network architecture is capable of pixel-level image segmentation, that is, it classifies each location of strain measurement as damaged or undamaged. The CNN which is fed full-field strain measurements as an input image of size 256 × 256 projects the SSD onto an output image of the same size. The data for network training is generated by numerical simulation of aluminum bars with different damage scenarios, including single damage and double damage cases at a random location, direction, length, and thickness. The trained network achieves an Intersection over Union (IoU) score of 0.790 for the validation set and 0.794 for the testing set. To check the applicability of the trained network on materials other than aluminum, testing is performed on a numerically generated steel dataset. The IoU score is 0.793, the same as the aluminum dataset, affirming the network's capability to apply to materials exhibiting a similar stress-strain relationship. To check the generalization potential of the network, it is tested on triple damage cases; the IoU score is found to be 0.764, suggesting that the network works well for unseen damage patterns as well. The network was also found to provide accurate predictions for real experimental data obtained from Strain Sensing Smart Skin (S4). This proves the efficacy of the network to work in real-life scenarios utilizing the full potential of the novel full-field strain sensing methods such as S4. The performance of the proposed network affirms that it can be used as a non-destructive testing method for subsurface crack detection and localization.

Improvement of lateral property of unidirectional-strengthened CFRP laminates using recycled carbon fiber

The unidirectional carbon fiber reinforced polymer (UD-CFRP) lacks the modulus of elasticity and strength in the lateral direction. This study investigates whether matrix resin with CFRP waste, recycled carbon fiber (rCF), can improve the lateral properties of CFRP. In total, twelve CFRP strips specimen were prefabricated of unidirectional carbon fiber (CF) sheet by hand lay-up (HLU) method and were tested by tensile test and X-ray computed tomography (X-ray CT). Factors such as fiber direction and void distribution significantly affecting its mechanical properties are assessed by X-ray CT inspection. It can be seen that rCF is mixed in a random direction at the position filled with matrix resin without rCF. However, a similar frequency of unimpregnation and voids can be observed in both specimens. Test results showed that experimental values of CFRP laminates with rCF-mixed matrix resin increased compared to the CFRP laminates without rCF. The percentage increase in the lateral tensile strength and modulus of elasticity of the rCFRP compared to the control specimen without rCF is 27.36% and 10.62%, respectively. This study proved that rCF can increase the lateral properties of unidirectional CFRP and shows the effective use of rCF for strengthening material in construction applications.

The Mechanoreception in Drosophila melanogaster Oocyte under Modeling Micro- and Hypergravity.

The hypothesis about the role of the cortical cytoskeleton as the primary mechanosensor was tested. Drosophila melanogaster oocytes were exposed to simulated microgravity (by 3D clinorotation in random directions with 4 rotations per minute-sµg group) and hypergravity at the 2 g level (by centrifugal force from one axis rotation-hg group) for 30, 90, and 210 min without and with cytochalasin B, colchicine, acrylamide, and calyculin A. Cell stiffness was measured by atomic force microscopy, protein content in the membrane and cytoplasmic fractions by Western blotting, and cellular respiration by polarography. The obtained results indicate that the stiffness of the cortical cytoskeleton of Drosophila melanogaster oocytes decreases in simulated micro- (after 90 min) and hypergravity (after 30 min), possibly due to intermediate filaments. The cell stiffness recovered after 210 min in the hg group, but intact microtubules were required for this. Already after 30 min of exposure to sµg, the cross-sectional area of oocytes decreased, which indicates deformation, and the singed protein, which organizes microfilaments into longitudinal bundles, diffused from the cortical cytoskeleton into the cytoplasm. Under hg, after 30 min, the cross-sectional area of the oocytes increased, and the proteins that organize filament networks, alpha-actinin and spectrin, diffused from the cortical cytoskeleton.

Adaptive H∞ event-triggered load frequency control in islanded microgirds with limited spinning reserve constraints

Using an islanded microgrid (MG) with large-scale integration of renewable energy is the most popular way of solving the reliable power supply problem for remote areas and critical electrical users. However, compared with traditional power systems, the limited spinning reserves and network communication bandwidth may cause weak frequency stability in the presence of stochastic renewable active outputs and load demand fluctuations. In this paper, an adaptive event-triggered control (ETC) strategy for a load frequency control (LFC) system in an islanded MG is proposed. First, a bounded adaptive event-triggered communication scheme is designed. This not only saves on network resources, but also ensures that the control center has a sensitive monitoring ability for the MG operating status when the frequency deviations have been effectively damped. Secondly, by fully considering the spinning reserve constraints and uncertain communication delays, the LFC system is described as a nonlinear model with saturation terms. Design criteria for ETC parameters are strictly deduced based on Lyapunov stability theory. Finally, an ETC parameter optimization algorithm based on random direction search is developed to reconcile the bandwidth occupancy and control performance. The effectiveness of the proposed method is verified in an MG test system.

Observation of high-energy neutrinos from the Galactic plane.

The origin of high-energy cosmic rays, atomic nuclei that continuously impact Earth's atmosphere, is unknown. Because of deflection by interstellar magnetic fields, cosmic rays produced within the Milky Way arrive at Earth from random directions. However, cosmic rays interact with matter near their sources and during propagation, which produces high-energy neutrinos. We searched for neutrino emission using machine learning techniques applied to 10 years of data from the IceCube Neutrino Observatory. By comparing diffuse emission models to a background-only hypothesis, we identified neutrino emission from the Galactic plane at the 4.5σ level of significance. The signal is consistent with diffuse emission of neutrinos from the Milky Way but could also arise from a population of unresolved point sources.

Use of 3D Optical Techniques in the Analysis of the Effect of Adding Rubber Recyclate to the Matrix on Selected Strength Parameters of Epoxy–Glass Composites

Abstract The article presents a method of modifying the strength properties of epoxy–glass composite by changing the percentage composition of the matrix by the addition of rubber recyclate. Taking into account environmental protection and economic conditions in the process of recycling and utilisation of waste, it is advisable to look for applications of non-degradable waste materials. Based on epoxy resin, a glass mat with a random direction of fibres and rubber recyclate, a test material with different percentage compositions was produced. Samples from the manufactured materials were subjected to a static tensile test on a ZwickRoell testing machine using the ARAMIS SRX measuring system. In addition, CT (computerized tomography) scans of the inside of the samples were made using a ZEISS METROTOM 6 Scout tomograph, and observations of the internal structures were made using a scanning electron microscope. The use of optical and microscopic techniques enabled the precise determination of strength parameters of the examined composites and the analysis of the behaviour of samples under load. The analysis of deformations over time in the examined samples showed a beneficial effect of the addition of rubber recyclate on the elastic properties of the examined composites.

Glass fiber/polytetrafluoroethylene composite with low dielectric constant and thermal stability for high-frequency application

Dielectric composites with low dielectric constants (ε), low dielectric loss (tan δ), and excellent thermal-mechanical stability are highly desired for high signal transmission speed and high device integration. Polytetrafluoroethylene (PTFE) based composites exhibit tremendous potential due to the outstanding dielectric properties and high-frequency stability of PTFE. However, achieving PTFE-based dielectric composites with thermal-mechanical stability and low dielectric constant is challenging. Herein, we develop a glass fiber/polytetrafluoroethylene (GF/PTFE) composite for high-frequency communication applications with a designed skeleton structure. By the paper-making process, the glass fiber can be dispersed in random directions to set up a skeleton with the PTFE matrix entering the space in the fiber network. The modification of GF was also carried out to low down the dielectric loss of the composite. Benefiting from the supporting effect of the skeleton structure and surface modification, the thermal-mechanical stable dielectric composite was achieved with a low dielectric constant, and dielectric loss remained (m-GF content = 15 wt%, ε = 2.326 and tan δ = 5.24 × 10−3 at 30 GHz, xy-CTE = 29 ppm/°C and z-CTE = 47 ppm/°C). Moreover, the composites also have good dielectric constant frequency stability and thermal conductivity. Thus, this work provided an effective way to tailor the structure of functional composites with disordered fiber fillers.

Multiple timescales of learning indicated by changes in evidence-accumulation processes during perceptual decision-making

Evidence accumulation models have enabled strong advances in our understanding of decision-making, yet their application to examining learning has not been common. Using data from participants completing a dynamic random dot-motion direction discrimination task across four days, we characterized alterations in two components of perceptual decision-making (Drift Diffusion Model drift rate and response boundary). Continuous-time learning models were applied to characterize trajectories of performance change, with different models allowing for varying dynamics. The best-fitting model included drift rate changing as a continuous, exponential function of cumulative trial number. In contrast, response boundary changed within each daily session, but in an independent manner across daily sessions. Our results highlight two different processes underlying the pattern of behavior observed across the entire learning trajectory, one involving a continuous tuning of perceptual sensitivity, and another more variable process describing participants’ threshold of when enough evidence is present to act.

The Impact of Mobility on Physical Layer Security of 5G IoT Networks

Internet of Things (IoT) is rapidly spreading and reaching a multitude of different domains, since the fifth generation (5G) wireless technologies are the key enablers of many IoT applications. It is hence apparent that the broadcast nature of IoT devices makes data security unprecedentedly critical. Compared with traditional cryptography algorithms, which cannot cater for the features of IoT devices characterized by the severe limits in terms of energy, computation and storage capabilities, physical layer security (PLS) has been regarded as a promising solution to facilitate secure communications by exploiting the intrinsic randomness of the wireless medium. However, most of previous works assumed that all devices are static, and the impact of mobility on PLS deserves further investigation. In this paper, applying two types of random mobile models, i.e., the models of Random WayPoint (RWP) and Random Direction (RD), we study the impact of mobility on PLS in a scenario with three types of wireless devices (i.e., a destination, multiple interferers and an eavesdropper). Specifically, we establish an analytical framework for secrecy transmission capacity (STC), a fundamental metric in the study of PLS, under RWP and RD models. To the best of our knowledge, this is the first paper to derive STC and present the condition to achieve a positive STC with the consideration of mobility. We conclude that the RWP mobile destination can achieve a higher STC than that achievable in RD mobile and static scenarios, while RWP mobile eavesdropper is a challenging scenario to obtain a positive STC. Therefore, we propose an effective secrecy improvement strategy for the latter. Simulation validates the theoretical analyses.