Average Vector Research Articles

Fast Motion State Estimation Based on Point Cloud by Combing Deep Learning and Spatio-Temporal Constraints

Moving objects in the environment have a higher priority and more challenges in growing domains like unmanned vehicles and intelligent robotics. Estimating the motion state of objects based on point clouds in outdoor scenarios is currently a challenging area of research. This is due to factors such as limited temporal information, large volumes of data, extended network processing times, and the ego-motion. The number of points in a point cloud frame is typically 60,000–120,000 points, but most current motion state estimation methods for point clouds only downsample to a few thousand points for fast processing. The downsampling step will lead to the loss of scene information, which means these methods are far from being used in practical applications. Thus, this paper proposes a motion state estimation method that combines spatio-temporal constraints and deep learning. It starts by estimating and compensating the ego-motion of multi-frame point cloud data and mapping multi-frame data to a unified coordinate system; then the point cloud motion segmentation model on the multi-frame point cloud is proposed for motion object segmentation. Finally, spatio-temporal constraints are utilized to correlate the moving object at different moments and estimate the motion vectors. Experiments on KITTI, nuScenes, and real captured data show that the proposed method has good results, with an average vector deviation of only 0.036 m and 0.043 m in KITTI and nuScenes under a processing time of about 80 ms. The EPE3D error under the KITTI data is only 0.076 m, which proves the effectiveness of the method.

МГД-волны в области предфронта межпланетных ударных волн 6 и 7 сентября 2017 г.

We analyze strong space weather disturbances during first ten days of September 2017, using the geomagnetic Dst index, parameters of normals to interplanetary shock fronts, direct measurements of interplanetary magnetic field, solar wind, and cosmic ray parameters. By applying spectral analysis methods to interplanetary medium data, we analyze MHD waves at the pre-front of two interplanetary shocks responsible for geomagnetic disturbances on September 6 and 7, 2017. The main results are as follows: the contribution of three branches of MHD waves (Alfvén, fast and slow magnetosonic) to the observed spectrum of the interplanetary magnetic field modulus has been established. We have confirmed the conclusion that the generation of Alfvén waves and fast magnetosonic waves is due to the presence of low-energy proton fluxes (Ep~1 MeV) at the pre-front of interplanetary shocks. We have also discovered a predominant contribution of slow magnetosonic waves to the observed spectrum of the interplanetary magnetic field modulus, but its reason is yet unknown. It is noted that different orientations of the normals to the interplanetary shock fronts and to the direction of the interplanetary magnetic field average vector on spacecraft located fairly close to each other may indicate waviness of the shock front structure.

MHD waves at the pre-front of interplanetary shocks on September 6 and 7, 2017

We analyze strong space weather disturbances during first ten days of September 2017, using the geomagnetic Dst index, parameters of normals to interplanetary shock fronts, direct measurements of interplanetary magnetic field, solar wind, and cosmic ray parameters. By applying spectral analysis methods to interplanetary medium data, we analyze MHD waves at the pre-front of two interplanetary shocks responsible for geomagnetic disturbances on September 6 and 7, 2017. The main results are as follows: the contribution of three branches of MHD waves (Alfvén, fast and slow magnetosonic) to the observed spectrum of the interplanetary magnetic field modulus has been established. We have confirmed the conclusion that the generation of Alfvén waves and fast magnetosonic waves is due to the presence of low-energy proton fluxes (Ep~1 MeV) at the pre-front of interplanetary shocks. We have also discovered a predominant contribution of slow magnetosonic waves to the observed spectrum of the interplanetary magnetic field modulus, but its reason is yet unknown. It is noted that different orientations of the normals to the interplanetary shock fronts and to the direction of the interplanetary magnetic field average vector on spacecraft located fairly close to each other may indicate waviness of the shock front structure.

Community detection using Jaya optimization algorithm based on deep learning methods and KNN graph-based clustering

Purpose In this paper, community identification has been considered as the most critical task of social network analysis. The purpose of this paper is to organize the nodes of a given network graph into distinct clusters or known communities. These clusters will therefore form the different communities available within the social network graph. Design/methodology/approach To date, numerous methods have been developed to detect communities in social networks through graph clustering techniques. The k-means algorithm stands out as one of the most well-known graph clustering algorithms, celebrated for its straightforward implementation and rapid processing. However, it has a serious drawback because it is insensitive to initial conditions and always settles on local optima rather than finding the global optimum. More recently, clustering algorithms that use a reciprocal KNN (k-nearest neighbors) graph have been used for data clustering. It skillfully overcomes many major shortcomings of k-means algorithms, especially about the selection of the initial centers of clusters. However, it does face its own challenge: sensitivity to the choice of the neighborhood size parameter k, which is crucial for selecting the nearest neighbors during the clustering process. In this design, the Jaya optimization method is used to select the K parameter in the KNN method. Findings The experiment on real-world network data results show that the proposed approach significantly improves the accuracy of methods in community detection in social networks. On the other hand, it seems to offer some potential for discovering a more refined hierarchy in social networks and thus becomes a useful tool in the analysis of social networks. Originality/value This paper introduces an enhancement to the KNN graph-based clustering method by proposing a local average vector method for selecting the optimal neighborhood size parameter k. Furthermore, it presents an improved Jaya algorithm with KNN graph-based clustering for more effective community detection in social network graphs.

Clinical implementation and evaluation of stereotactic liver radiotherapy in inspiration breath-hold using nasal high flow therapy and surface guidance.

To evaluate two years of clinical experience with markerless breath-hold liver stereotactic radiotherapy (SBRT) using non-invasive nasal high flow therapy (NHFT) for breath-hold prolonging and surface guidance (SGRT) for monitoring. Heated and humidified air was administered via a nasal cannula (40 L/min, 80% oxygen, 34 °C). Patients performed voluntary inspiration breath-holds with visual feedback. After a training session, 4-5 breath-hold CT scans were acquired to delineate an internal target volume (ITV) accounting for inter- and intra-breath-hold variations. Patients were treated in 3-8 fractions (7.5-20 Gy/fraction) using SGRT-controlled beam-hold. Patient setup was performed using SGRT and CBCT imaging. A post-treatment CBCT was acquired for evaluation purposes. Fifteen patients started the training session and received treatment, of whom 10 completed treatment in breath-hold. Half of all 60-second CBCT scans were acquired during a single breath-hold. The average maximum breath-hold duration during treatment ranged from 47-108 s. Breath-hold ITV was on average 6.5 cm³/30% larger (range: 1.1-23.9 cm³/5-95%) than the largest GTV. Free-breathing ITV based on 4DCT scans was on average 16.9 cm³/47% larger (range: -2.3-58.7 cm3/-16-157%) than the breath-hold ITV. The average 3D displacement vector of the area around PTV for the post-treatment CBCT scans was 5.0 mm (range: 0.7-12.9 mm). Liver SBRT in breath-hold using NHFT and SGRT is feasible for the majority of patients. An ITV reduction was observed compared to free-breathing treatments. To further decrease the PTV, internal anatomy-based breath-hold monitoring is desired. Non-invasive NHFT allows for prolonged breath-holding during surface-guided liver SBRT.

Enhancing Oral Squamous Cell Carcinoma Detection Using Histopathological Images: A Deep Feature Fusion and Improved Haris Hawks Optimization-Based Framework.

Oral cancer, also known as oral squamous cell carcinoma (OSCC), is one of the most prevalent types of cancer and caused 177,757 deaths worldwide in 2020, as reported by the World Health Organization. Early detection and identification of OSCC are highly correlated with survival rates. Therefore, this study presents an automatic image-processing-based machine learning approach for OSCC detection. Histopathological images were used to compute deep features using various pretrained models. Based on the classification performance, the best features (ResNet-101 and EfficientNet-b0) were merged using the canonical correlation feature fusion approach, resulting in an enhanced classification performance. Additionally, the binary-improved Haris Hawks optimization (b-IHHO) algorithm was used to eliminate redundant features and further enhance the classification performance, leading to a high classification rate of 97.78% for OSCC. The b-IHHO trained the k-nearest neighbors model with an average feature vector size of only 899. A comparison with other wrapper-based feature selection approaches showed that the b-IHHO results were statistically more stable, reliable, and significant (p < 0.01). Moreover, comparisons with those other state-of-the-art (SOTA) approaches indicated that the b-IHHO model offered better results, suggesting that the proposed framework may be applicable in clinical settings to aid doctors in OSCC detection.

Quantum-Dash Semiconductor Optical Amplifier for Millimeter-Wave over Fibre Wireless Fronthaul Systems

This paper demonstrates a five-layer InAs/InP quantum-dash semiconductor optical amplifier (QDash-SOA), which will be integrated into microwave-photonic on-chip devices for millimeter-wave (mmWave) over fibre wireless networking systems. A thorough investigation of the QDash-SOA is conducted regarding its communication performance at different temperatures, bias currents, and input powers. The investigation shows a fibre-to-fibre (FtF) small-signal gain of 18.79 dB and a noise figure of 6.3 dB. In a common application with a 300 mA bias current and 25 °C temperature, the peak FtF gain is located at 1507.8 nm, which is 17.68 dB, with 3 dB gain bandwidth of 56.6 nm. Furthermore, the QDash-SOA is verified in a mmWave radio-over-fibre link with QAM (32 Gb/s 64-QAM 4-GBaud) and OFDM (250 MHz 64-QAM) signals. The average error vector magnitude of the QAM and OFDM signals after a 2 m wireless link could be as low as 8.29% and 6.78%, respectively. These findings highlight the QDash-SOA’s potential as a key amplifying component in future integrated microwave-photonic on-chip devices.

An improved container-based deep forest model for predicting groundwater recharge

Abstract This paper proposes ICDF, an improved container-based deep forest model, for effectively modeling and predicting groundwater recharge. The model consists of four points: the construction and expansion of the container module, the assignment of weights to the base model, the growth of the cascade layer, and the decision output. First, container modules are created and a maximization objective function is assigned to each container to control its growth. Next, different weights are assigned to each base model based on its contribution to container prediction. Cascade layers are built using container modules until the model prediction decreases. Finally, the average prediction vectors of the last cascade layer are output. The model’s performance is evaluated and compared with DF and its base models (random forest, adaptive boosting, and extreme gradient boosting) using a case study of 1549 bores in New South Wales, Australia. Remarkably, compared to DF, ICDF has improved prediction accuracy by 6.66%. Moreover, it outperformed the RF, AdaBoost, and XGBoost by 2.94%, 5.85%, and 5.3% in prediction performance, respectively. The ICDF exhibited superior capabilities in predicting groundwater recharge, offering significant improvement over existing models. Practitioners are encouraged to consider adopting ICDF for groundwater management.

High order energy-preserving method for the space fractional Klein–Gordon-Zakharov equations

The space fractional Klein–Gordon-Zakharov equations are transformed into the multi-symplectic structure system by introducing new auxiliary variables. The multi-symplectic system, which satisfies the multi-symplectic conservation, local energy and momentum conservation, is discretizated into the semi-discrete multi-symplectic system by the Fourier pseudo-spectral method. The second order multi-symplectic average vector field method is applied to the semi-discrete system. The fully discrete energy preserving scheme of the space fractional Klein–Gordon-Zakharov equation is obtained. Based on the composition method, a fourth order energy preserving scheme of the Riesz space fractional Klein–Gordon-Zakharov equations is also obtained. Numerical experiments confirm that these new schemes can have computing ability for a long time and can well preserve the discrete energy conservation property of the equations.

Shadow removal method for high-resolution aerial remote sensing images based on region group matching

Shadow removal is a critical step in preprocessing high-resolution aerial remote sensing images. The presence of shadows reduces the information in high-resolution aerial remote sensing images, lowers the image quality, and seriously interferes with the work of feature classification, object detection, and information extraction. Therefore, this paper proposes a high-resolution aerial remote sensing image shadow removal method based on region group matching. The method starts from both spatial information and color information, and is able to take into account the whole and the local, and recover the detailed features of different features inside the shadows. Firstly, this paper proposes an irregular region color transfer method based on three-dimensional color space, which can effectively restore the original color and texture information of the features in the shadow region. Secondly, considering the random and variable direction of feature distribution in remote sensing images, the direction of texture feature extraction cannot be set in advance. Therefore, in this paper, the texture details of the features are extracted based on the rotationally invariant light-independent texture feature extraction method to exclude the abnormal interference. Then, the shadow and light regions of the image are segmented internally and grouped according to the type of features to avoid subsequent matching anomalies due to the small size of the image blocks. Next, construct an average texture feature vector for each group of image blocks for grouping matching. The matched light groups are then used to guide the shadow groups for local feature information enhancement, which ensures that on top of the overall shadow removal, the detail recovery of different local features is enhanced. Finally, for the boundary effect after shadow removal, a boundary optimization method with dynamically assigned weights is proposed, which can ensure the natural coherence of the resultant image in the boundary part. The experimental results show that the proposed method balances both overall and local shadow removal effects and can effectively achieve high-resolution aerial remote sensing image shadow removal. Compared with existing methods, the proposed method achieves the best quality of shadow removal results and restores more realistic ground features in shadow region.

Large-Scale Side By Side Stereo-PIV Measurements In An Automotive Wind Tunnel: Aerodynamic Influence Of Ride Height Variations.

In this investigation, we conducted large-scale Stereo PIV experiments in the flow around a Volkswagen ID.4. within the Aerodynamic and Aeroacoustic Wind Tunnel (AAK) at Volkswagen AG, Wolfsburg. The primary objective of these experiments was to investigate the effects of ride height variation on the near wake of the passenger vehicle using Stereo PIV, while minimizing wind tunnel occupancy. The measurements aimed to create datasets for CFD validation to improve virtual aerodynamic development capabilities of passenger vehicles. The experiment was performed on a 1:1 scale vehicle and multiple cross-sectional planes were scanned along the longitudinal axis of the vehicle. The impact of ride height alterations on the wake flow topology were evaluated. Average velocity vector fields were computed per case, providing comprehensive insights into the aerodynamic effects of diverse vehicle setups. A small change in the ride height has shown to have a significant impact on the wake topology. The results show that the electric SUV ID.4 can yield a wake topology similar to the estate back and fast/notchback type of vehicle, depending on the ride height. The normal ride height of the ID.4 shows to have a broader but lower wake region, with a significant downwash. The PIV results of this configuration show significant similarities to the wake topology of a fast back vehicle. Lowering the ride height by 15 mm has shown already to be enough to cause a global effect on the wake topology and to exhibit a narrower wake and recirculation region, like an estate back vehicle. Additionally, the association between the drag/lift coefficients and the wake topology of both configurations was discussed. Results have shown that sole measurements in the wake are not sufficient to fully explain changes in the determined drag or front lift coefficients. The rear lift coefficients however, indicate a stronger relationship with the near wake topology of the vehicle.

Students’ understanding of vector operations: With and without physics education technology simulation

This study aims to investigate the influence of technology integration (physics education technology simulation) in physics learning. A quantitative method with a quasi-experimental post-test-only design was applied to reveal the differences in students’ abilities to operate vectors between the group that learned and tested using simulation and the group that learned using graphics only. The test consisted of 16 questions on the addition and subtraction of two vectors divided into one and two dimensions. Student response data were analyzed using descriptive statistics and Kruskal-Wallis test. The analysis results show that the vector operation score for experimental group 1, which learned vector operations through graphic learning using simulation and tested with the same simulation (86.15), is higher than experimental group 2, which learned vector operations through graphic learning using simulation (76.59), and the control group, which learned vector operations through graphic learning without simulation assistance (71.81). All three groups have higher average scores for one-dimensional than two-dimensional vector operations. They also have higher average scores for vector addition compared to vector subtraction operations. Kruskal-Wallis test results indicate a significant difference in the average vector operation scores for the three groups (ρ=0.021). This suggests that integrating technology in the learning and testing processes leads to a significant difference. Moreover, the difference is significant for one-dimensional vector operations (ρ=0.004) and not significant for two-dimensional vector operations (ρ=0.107). These findings could support the implementation of similar approaches at the university and high school levels, especially for vector-related topics, in both learning and testing processes.

New insights into ductility improvement of a nickel-based superalloy through grain boundary engineering

Grain boundary engineering (GBE) provides an effective approach for tailoring the properties of metallic materials by optimizing the grain boundary character distribution (GBCD). Using electron backscatter diffraction analysis and uniaxial tensile testing at room temperature, this study systematically investigated the impact of GBE-type thermomechanical processing (TMP) on the ductility of an Incoloy 925 nickel-based superalloy (Alloy 925). The results showed that at 5 % strain, increasing the annealing temperature results in larger twin-related domains through strain-induced boundary migration. The GBE-type TMP can significantly enhance the ductility of Alloy 925, namely, the elongation increases from 64.22 % to 95.86 % as the annealing temperature increases from 1050 °C to 1100 °C. The evolved GBCD introduces more coherent twin boundaries with lower average residual Burgers vectors, enabling efficient dislocation transmission. In addition, extensive twinning can widely generate some soft orientations and simultaneously reduce the average Taylor factor within the twin clusters, leading to easy dislocation activation during plastic deformation. The synergistic effect of the optimized grain boundary network and twin cluster anisotropy promotes uniform plastic flow and effective strain accommodation, thereby enhancing the ductility of Alloy 925. Overall, this work provides novel and valuable insights into ductility optimization through GBE in a nickel-based superalloy.

Structure-preserving algorithms for guiding center dynamics based on the slow manifold of classical Pauli particle

The classical Pauli particle (CPP) serves as a slow manifold, substituting the conventional guiding center dynamics. Based on the CPP, we utilize the averaged vector field (AVF) method in the computations of drift orbits. Demonstrating significantly higher efficiency, this advanced method is capable of accomplishing the simulation in less than one-third of the time of directly computing the guiding center motion. In contrast to the CPP-based Boris algorithm, this approach inherits the advantages of the AVF method, yielding stable trajectories even achieved with a tenfold time step and reducing the energy error by two orders of magnitude. By comparing these two CPP algorithms with the traditional RK4 method, the numerical results indicate a remarkable performance in terms of both the computational efficiency and error elimination. Moreover, we verify the properties of slow manifold integrators and successfully observe the bounce on both sides of the limiting slow manifold with deliberately chosen perturbed initial conditions. To evaluate the practical value of the methods, we conduct simulations in non-axisymmetric perturbation magnetic fields as part of the experiments, demonstrating that our CPP-based AVF method can handle simulations under complex magnetic field configurations with high accuracy, which the CPP-based Boris algorithm lacks. Through numerical experiments, we demonstrate that the CPP can replace guiding center dynamics in using energy-preserving algorithms for computations, providing a new, efficient, as well as stable approach for applying structure-preserving algorithms in plasma simulations.

End-to-End Top-Down Load Forecasting Model for Residential Consumers

This study presents an efficient end-to-end (E2E) learning approach for the short-term load forecasting of hierarchically structured residential consumers based on the principles of a top-down (TD) approach. This technique employs a neural network for predicting load at lower hierarchical levels based on the aggregated one at the top. A simulation is carried out with 9 (from 2013 to 2021) years of energy consumption data of 50 houses located in the United States of America. Simulation results demonstrate that the E2E model, which uses a single model for different nodes and is based on the principles of a top-down approach, shows huge potential for improving forecasting accuracy, making it a valuable tool for grid planners. Model inputs are derived from the aggregated residential category and the specific cluster targeted for forecasting. The proposed model can accurately forecast any residential consumption cluster without requiring any hyperparameter adjustments. According to the experimental analysis, the E2E model outperformed a two-stage methodology and a benchmarked Seasonal Autoregressive Integrated Moving Average (SARIMA) and Support Vector Regression (SVR) model by a mean absolute percentage error (MAPE) of 2.27%.

Balancing homophily and prejudices in opinion dynamics: An extended Friedkin–Johnsen model

In this paper we propose an extended version of the Friedkin–Johnsen (FJ) model where we analyze the effects that a homophily-based influence matrix has on the opinion formation process. In particular, we assume that the influence matrix varies over time, and its entries (that represent the appraisals that individuals have of the other individuals) update based on the comparison between their opinions and can take both positive and negative values. This leads to a system of two difference equations, where the first one corresponds to the standard FJ model, except that the influence matrix is no longer constant, and the second one models the dynamics of this matrix. We show that a necessary and sufficient condition for the convergence of this modified version of the classical FJ model is that the influence matrix becomes constant in a finite number of steps. Moreover, we provide the explicit expression for the agents’ asymptotic opinions in some special cases: the purely cooperative setting, the case of a structurally balanced network and the case of a single discussion topic. Finally, the case when the topics are correlated and the influence matrix depends on the average opinion vectors is investigated.

Vectorial Analysis of Deep Plane Face and Neck Lift.

The vector of aging and consequently the vector of lift in rhytidectomy has aided surgeons in improving movement of tissues during facial rejuvenation procedures. The goal was to analyze the vector of lift in patients undergoing primary and revisional facelift to achieve proper vectorial lifting. Patients undergoing deep-plane facelift surgery were included for analysis. Intraoperative photographs and measurements were taken of the skin, superficial musculoaponeurotic system (SMAS), and platysmal suture suspension with mastoid crevasse inset. Measurements were compared between patients who were undergoing primary vs secondary surgery, site of lift, age, and gender. Seventy-one patients (90% female, mean age 57.8) with a total of 142 hemifaces were analyzed, 57 (73%) of which were primary and 14 (27%) secondary facelifts. The average vector of SMAS lifting was 70.8°. Females had a more vertical vector than males (71.3° vs 65.4°; P < .01). The average vectors of platysmal and skin lift were 87.0° and 58.2°, respectively. There was intrapatient difference between hemifaces. Despite there being more intersuture disparity in secondary cases than primary cases (16.9° vs 4.5°; P < .05), the mean vector of lifting was similar between them. Proper release of the deep plane helps determine the appropriate vectors of lift, without relying on guidelines based on population averages. Each patient presents with a unique vector required to correct their descent. This technique provides an optimal result by directly suspending against the vectors of greatest descent.

A Time Series Prediction Model for Wind Power Based on the Empirical Mode Decomposition–Convolutional Neural Network–Three-Dimensional Gated Neural Network

In response to the global challenge of climate change and the shift away from fossil fuels, the accurate prediction of wind power generation is crucial for optimizing grid operations and managing energy storage. This study introduces a novel approach by integrating the proportional–integral–derivative (PID) control theory into wind power forecasting, employing a three-dimensional gated neural (TGN) unit designed to enhance error feedback mechanisms. The proposed empirical mode decomposition (EMD)–convolutional neural network (CNN)–three-dimensional gated neural network (TGNN) framework starts with the pre-processing of wind data using EMD, followed by feature extraction via a CNN, and time series forecasting using the TGN unit. This setup leverages proportional, integral, and differential control within its architecture to improve adaptability and response to dynamic wind patterns. The experimental results show significant improvements in forecasting accuracy; the EMD–CNN–TGNN model outperforms both traditional models like autoregressive integrated moving average (ARIMA) and support vector regression (SVR), and similar neural network approaches, such as EMD–CNN–GRU and EMD–CNN–LSTM, across several metrics including mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2). These advancements substantiate the model’s effectiveness in enhancing the precision of wind power predictions, offering substantial implications for future renewable energy management and storage solutions.

Improvement of microstructure of granular soil in the densification process caused by impact loading

Improvement of the mechanical properties of soil, such as density, compressibility and shear strength, is typically due to the variation in its inherent microstructure. This paper presents a microscopic study of the densification mechanism in granular soils subjected to impact loading. Both model test and discrete element simulation were carried out to quantitatively analyze the fabric evolution from a particle-scale perspective. Irregularly shaped particles were used in the simulation, on basis of which realistic packing structural information such as average contact number, contact area and branch vector length could be learned. The results reveal the microscopic densification mechanism that impact loading not only promotes the increase of contact number, but also enhances the contact area around per particle. Increasing of contact area has enriched the distribution of sutured contacts to form more steady mechanical status of soil.

Linearly-fitted energy-mass-preserving schemes for Korteweg–de Vries equations

In this paper, second-order and fourth-order linearly-fitted energy-mass-preserving schemes for Korteweg–de Vries (KdV) equations are proposed. First, the KdV equation is semi-discretized into an oscillatory Hamiltonian system of Ordinary Differential Equations (ODEs) by semi-discretizations of the skew adjoint operator and the Hamiltonian in the Hamiltonian form of the KdV equation. Then the resulting semi-discrete system is integrated by exponential average vector field integrator. The order of convergence and the conservation properties are established for the introduced full-discrete procedures. It shows that the proposed methods are energy-preserving, mass-preserving and qualitatively preserve the dispersive relation well. Numerical experiments are carried out to validate the efficiency and the accuracy of the proposed schemes.