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Point clouds, consisting of geometry and attributes, are an emerging media format essential to the deployment of augmented reality applications. Additionally, the large data volume of point clouds poses severe challenges for efficient storage and transmission. In this paper, we borrow the idea of end-to-end deep-network-based image/video coding and propose the first--to our best knowledge--end-to-end deep framework for compressing point cloud attributes. Specifically, we propose a point cloud lossy attribute autoencoder, which directly encodes and decodes point cloud attributes with the help of geometry, instead of voxelizing or projecting the points. In the autoencoder, we propose a second-order point convolution that improves the general point convolution in previous point-based learning methods; the proposed convolution utilizes the spatial correlations between more points and the nonlinear relationship between attribute features. We also introduce a dense point-inception block, which derives from a combination of an inception-style block and a dense block, to improve feature propagation. In addition, we devise a multiscale loss to guide the autoencoder in focusing attention on the coarse-grained points with better coverage of the entire point cloud, which makes it easier for the autoencoder to obtain better optimization of the qualities of all points. Experimental results show that our proposed framework still has a performance gap compared with the state-of-the-art algorithms in the MPEG G-PCC reference software TMC13. However, it does outperform the region-adaptive hierarchical transform with a run-length Golomb-Rice entropy coder (RAHTRLGR), which is one of the core transforms used in TMC13 without many well-designed techniques that make TMC13 what it is today. It outperforms RAHT-RLGR by 2.63 dB, 1.77 dB, and 3.40 dB on average in terms of the Bjntegaard delta peak signal-to-noise ratio (BD-PSNR) for the Y, U, and V components on the test dataset. A subjective quality comparison demonstrates the advantages of our framework in preserving more local textures and reducing blocking and color noise artifacts.

Ship detection in remote sensing images plays a crucial role in various applications and has drawn increasing attention in recent years. However, existing arbitrary-oriented ship detection methods are generally developed on a set of predefined rotated anchor boxes. These predefined boxes not only lead to inaccurate angle predictions but also introduce extra hyper-parameters and high computational cost. Moreover, the prior knowledge of ship size has not been fully exploited by existing methods, which hinders the improvement of their detection accuracy. Aiming at solving the above issues, in this paper, we propose a center-head point extraction based detector (named CHPDet) to achieve arbitrary-oriented ship detection in remote sensing images. Our CHPDet formulates arbitrary-oriented ships as rotated boxes with head points which are used to determine the direction. And rotated Gaussian kernel is used to map the annotations into target heatmaps. Keypoint estimation is performed to find the center of ships. Then, the size and head point of the ships are regressed. The orientation-invariant model (OIM) is also used to produce orientation-invariant feature maps. Finally, we use the target size as prior to finetune the results. Moreover, we introduce a new dataset for multi-class arbitrary-oriented ship detection in remote sensing images at a fixed ground sample distance (GSD) which is named FGSD2021. Experimental results on FGSD2021 and two other widely used data sets, i.e., HRSC2016, and UCAS-AOD demonstrate that our CHPDet achieves state-of-the-art performance and can well distinguish between bow and stern. Code and FGSD2021 dataset are available at https://github.com/zf020114/CHPDet., Comment: Accepted by IEEE Transactions on Geoscience and Remote Sensing (TGRS)

This study presents a prediction of creep behavior concrete matrix based on waste glass powder during 2000 h of loading and describe a procedure for modeling the primary creep law and we have presented the intergranular contact simulation bodies using ANSYS software. The behavior of this type of concrete is in the reaction of pozzolan reactivity of waste glass powder, which acts as a substitute for cement powder. The modeling procedure of creep behavior consist to applied the finite element method (FEM) based on a model called (strain hardening model) using the computer code ansys14.0. This paper illustrates a new approach of study by the FEM of the intergranular concrete matrix on the microscopic level and to note closely the phenomena of creep behaviors. This item presents ANSYS ® modeling interface Glass Powder – granular in a concrete Matrix structure. Contact problems fall into two general classes: rigid-to-flexible and flexible-to-flexible. In general, any time a soft material comes in contact with a hard material, the problem may be assumed to be rigid-to-flexible. To model a contact problem, you first need to identify the parts to be analyzed for their possible interaction. If one of the interactions is at a point, the corresponding component of your model is a node. If one of the interactions is at a surface, the corresponding component of your model is an element. The finite element model recognizes possible contact pairs by the presence of specific contact elements. These contact elements are overlaid on the parts of the model that are being analyzed for interaction. ANSYS ® 14.0 software has been used to perform the numerical calculation in this paper.

Predicting the initiation type of oblique detonation waves (ODWs) is critical to the application of ODWs in engine, but there is still no fully analytical criterion on the initiation type. In this study, a theoretical criterion is proposed to predict the initiation type, whose physical foundation is compression wave convergence observed recently. The criterion depends on the ratio of two characteristic lengths, the height of compression wave intersection point, HCW, and the corresponding height of oblique shock wave (OSW) at the same x-location, HOSW. If the ratio is below 1, the compression wave converges quickly to induce abrupt initiation; otherwise, slow compression wave convergence leads to smooth initiation. Because these two parameters could be calculated theoretically, this is the first fully analytical criterion independent of numerical or experimental parameters. Numerical results demonstrate that the criterion performs well in hydrogen–air mixtures corresponding wide flight conditions. Further examinations in two kinds of hydrocarbon fuel-based mixtures under standard conditions demonstrates the universality of this criterion.

This study presents an approach to the problem of minimizing the impact of low vegetation on the accuracy of a UAV-derived DEM, based on the use of a deep neural network (DNN). It is proposed to use the U-Net network to determine corrections to the height of the raw point cloud so that the processed data reflect the actual earth's surface. The implemented solution is therefore based on regression, not classification. As a result of the proposed processing method, the expected value of the land surface height is determined for each point of the unified point cloud. In addition, a second U-Net network is trained, enabling the uncertainty of the corrected heights of the land surface to be determined for each point of the unified cloud. The training set includes data from different seasons, which makes the models more resistant and allows for assessment of the impact of the season and more generally the related vegetation status on the model accuracy. The processing results can be used in DEM generation, and also for determining the vertical displacements of the terrain surface associated with underground mining, as well as natural phenomena such as landslides. A key advantage of the proposed processing method is the ability to predict the uncertainty of the results.

We propose a robust geometry-constrained scale estimation approach for monocular visual odometry that takes the camera height as an absolute reference. Visual odometry is an essential module for robot self-localization and autonomous navigation in unexplored environments. Scale recovery is an indispensable requirement for monocular visual odometry as it makes up the metric information lost by the single camera and helps to reduce the scale drift. When the camera height is considered as the absolute reference, the precision of scale recovery depends on the accuracy of the road point selection and road geometric model calculation. However, most of the previous approaches solve these two problems sequentially, and their road point selection is based on the color model of the road or prior-knowledge-based fixed region. In this paper, we novelly propose to combine and iteratively solve these two problems. We adopt the geometric model, instead of the color model, of the road to select the road points. Furthermore, the selected road feature points are used to estimate the road model, which in turn limits the road point selection. In detail, we segment our feature points with Delaunay triangulation and select road points guided by the depth consistency and road model consistency. The experiments on the KITTI dataset show that our method achieves the best performance among state-of-the-art monocular visual odometry methods.

The control design for juggling a devil-stick between two symmetric configurations is proposed. Impulsive forces are applied to the devil-stick at the two configurations; and impulse of the force and its point of application are modeled as the control inputs. The dynamics of the devil-stick is described by a single return Poincare map and it is shown that the control objective of juggling can be achieved by stabilizing a hybrid orbit. The impulse controlled Poincare map (ICPM) approach, recently proposed for stabilization of continuous-time orbits of underactuated systems, is extended to achieve asymptotic stabilization of the hybrid orbit. The applicability of the ICPM approach to devil-stick juggling is demonstrated through numerical simulations.

Adaptive Kriging combined with Monte Carlo simulation (AK-MCS) is popularly used to estimate the failure probability, but its candidate sample pool (CSP) size should be large enough to guarantee the accuracy of failure probability estimation, especially for small failure probability. To select training point for updating the Kriging model, AK-MCS needs to traverse the CSP to calculate the U-learning function of every point, which is time-demanding. Thus, an enhanced AK-MCS (shorten as EAK-MCS Ⅰ) is proposed by use of an efficient CSP reduction strategy. In EAK-MCS Ⅰ, the candidate samples are classified into two types, i.e., the samples with accurately judged states and the other samples. Obviously, removing the first type samples from CSP does not need extra computational cost, which has no effect on the precision of estimating failure probability and can improve the efficiency of training Kriging model. Hence, EAK-MCS Ⅰ is more efficient than AK-MCS in estimating failure probability. Additionally, for the risk of misjudging the states of the samples by the Kriging model trained in the reduced CSP for complicated problem, another version of EAK-MCS Ⅰ (shorten as EAK-MCS Ⅱ) is proposed by nesting an adaptive strategy to calibrate the Kriging model. The proposed methods improve the efficiency of AK-MCS and inherit all advantages of AK-MCS.

Classifying tree species from point clouds acquired by light detection and ranging (LiDAR) scanning systems is important in many applications, including remote sensing, virtual reality, and forestry inventory. Compared with terrestrial laser scanning systems, airborne laser scanning (ALS) systems can acquire large-scale tree point clouds from only a single scan. However, ALS point clouds have the disadvantages of low density, uneven distribution, and unclear branch structure, making the classification of tree species from ALS point clouds a challenging task. Recently, deep learning-based classification approaches, such as PointNet++, which can operate directly on 3-D point sets, have been intensively studied in scene classification. However, the classification precision of learning-based approaches for point clouds relies on point coordinates and features, such as normals. Unlike the face normals of regular objects, trees have complex branch structures and detailed leaves, which are difficult to capture using ALS systems. Hence, it might be inappropriate to use the normals of ALS tree points for classification. In this letter, we propose a novel convex hull-based feature descriptor for tree species classification using the deep learning network PointNet++. To evaluate the effectiveness of our approach, three additional feature descriptors (normal descriptor, alpha shape-based descriptor, and covariance descriptor) are also investigated with PointNet++. The results show that the convex hull-based feature descriptor can achieve 86.6% overall accuracy in tree species classification, which is notably higher than the other three descriptors.

Time-optimal trajectory planning can improve the efficiency of the manipulator, which has a paramount research significance. Nonetheless, almost all the current algorithms have the problem of path limitation – the algorithm constantly searches for the optimal motion time of the manipulator on a specific motion path. To solve this problem, we design a time-optimal trajectory planning method for the manipulator by searching the optimal path simultaneously. We propose using a cubic uniform B-spline interpolation algorithm to derive the motion curve expression of the manipulator joint with unknown path points. Then we use a genetic algorithm to optimize the path point and the motion time of the manipulator at the same time to get the time-optimal motion path of the manipulator. Moreover, we prove that the algorithm proposed in this paper is comparable with similar algorithms with known paths through experiments.

Quality of Service (QoS) is well acknowledged as a decisive means for ascertaining the performance of third-party Web services. QoS has high uncertainty in complex and dynamic network environments. QoS monitoring is considered as one of the most effective techniques to detect QoS violations at runtime. However, existing QoS monitoring approaches only consider single QoS attribute and do not provide a promising solution for comprehensively monitoring multivariate QoS attributes. To overcome this problem, a novel QoS monitoring approach, named M-BSRM (Multivariate BayeSian Runtime Monitoring), is proposed. First, M-BSRM adopts the point mutual information theory to initialize the weights of different environmental impact factors and solves the problem of uneven distribution between classes brought by traditional algorithms. Second, each single QoS attribute is integrated with user preference using the information fusion theory. Finally, a Bayesian classifier is used to comprehensively evaluate multivariate QoS attributes at runtime. The experimental results on both the real-world and simulated data sets show that M-BSRM is more effective, practical and efficient than the other approaches.

The combination of spectral and 3-D elevation information provided by hyperspectral images (HSIs) and Light Detection and Ranging (LiDAR) has gained increased attention in the remote sensing field and enabled numerous applications. While various methods have been proposed to fuse these two data streams in pixel, feature, or decision level, a deeper view into the intrinsic relation of surface geometry, material reflectance, and environment illumination is still lacking. In this article, we present a novel supervoxel-based joint intrinsic decomposition framework for HSIs and LiDAR. First, we proposed a novel intrinsic scene model for HSIs and LiDAR point cloud, which tells how we can map LiDAR point cloud into HSI pixels with point-cloud-level normals, reflectance, and incident light direction. Then, we extract supervoxels from the LiDAR point cloud using a graph-based supervoxel method. Finally, we formulate the intrinsic decomposition problem within a supervoxel-based framework which can be optimized effectively and efficiently. The outputs of the proposed model are intrinsic scene properties like incident light direction and point-cloud-level hyperspectral reflectance, with which we can then generate intrinsic hyperspectral point cloud (IHSPC) where each point possesses not only 3-D coordinates and normals but also the reflectance over each wavelength. The performance of our approach is demonstrated with both synthetic and real data.

We derive two families of low-thrust station-keeping control procedures based on the dynamical properties of the phase space near libration point orbits. The first family is obtained as the limit of impulsive maneuvers, and the second one by means of dynamically reshaping Floquet modes. Despite following different approaches, the geometrical analysis shows that all these strategies can reshape the dynamical structures about the libration point orbits and stabilize the motion. The numerical results prove their robustness in front of measurement uncertainties and large initial orbit injection errors. Moreover, the long-term behavior is analyzed by means of Poincaré maps, and the results are compared with the Hamiltonian-structure preserving control strategy. Not just restricted to the particular problem considered, the new methodology provides a general framework to analyze the geometric behavior of any low-thrust station-keeping control law and opens the possibility of compact on-board techniques., Masdemont thanks MINECO-FEDER for the grant MTM2018-100928 and the Catalan government for the grant 2017SGR-1049. G. Gómez thanks MINECO-FEDER for the grant PID2019-104851GB-I00 and the Catalan government for the grant 2017SGR-1374.

Different optimization techniques are available for advancement the different machining parameters of milling process. The milling operations were performed and various characteristics namely MRR and SR were measured. Present work reveals, this paper deals a comparison of GRA, Taguchi Methodology and Topsis Methodology. The target of this exploratory work is to further developing the operating boundaries on a solitary activity and analyze the solution. Accordingly, the advancement is finished with various ultimate objectives that the resulting upgraded boundaries will advance a compromise between the efficiency and surface quality. The outcomes got created the most doable blends of the cutting boundaries and the work piece material that gave the most un-surface unpleasantness. Henceforth, the fruitful finishing of this work gives a mathematical methodology which is approved tentatively. In this examination paper a correlation is done between gra, Topsis and Taguchi Technique. The point of this work is to discover streamlining the machining boundary on a solitary activity and analyze the results. In this way the progression is finished with the ultimate objective that the ensuing upgraded boundaries will advance a compromise between the efficiency and surface quality. It is visualized that the solution in this investigation will help makers in anticipating and limiting the greatness of surface unpleasantness over machining activities.

Road extraction from very high resolution (VHR) satellite images is one of the most important topics in the field of remote sensing. In this letter, we propose an efficient nonlocal LinkNet with nonlocal blocks (NLBs) that can grasp relations between global features. This enables each spatial feature point to refer to all other contextual information and results in more accurate road segmentation. In detail, our single model without any postprocessing like conditional random field (CRF) refinement performed better than any other published state-of-the-art ensemble model in the official DeepGlobe Challenge. Moreover, our nonlocal LinkNet (NL-LinkNet) beat the D-LinkNet, the winner of the DeepGlobe challenge (Demir et al., 2018), with 43% less parameters, less giga floating-point operations per seconds (GFLOPs), and shorter training convergence time. We also present empirical analyses on the proper usages of NLBs for the baseline model.

Line segment matching is important in applications that require recovering the 3D structure of objects (e.g., manmade objects in street-level scenarios). However, differentiating between true and false line matches is generally difficult without strong geometric constraints for line segments. Hence, additional constraints are forced to be used, sacrificing many true line matches. This study proposes a robust line segment matching method based on global projective transformation modeling. We develop a non-parametric motion regression formulation with a specially designed direct linear transformation-based cost function that reformulates the piecewise smoothly varying projective transformations as a global continuous model from highly noisy point matches. The resultant model can effectively approximate the real underlying image transformation and derive high-quality point matches. We apply the computed model and high-quality point matches to a point-correspondence-based line matching pipeline, which provides sufficient strict geometric constraints for first generating the pair-to-pair matches and then distilling the line-to-line matches. Extensive experiments conducted on two challenging line matching datasets show that the proposed method can obtain considerable correct line segment matches, outperforming the comparison methods in mean F-score by at least 15.5% on the benchmark dataset and 16.9% on the local dataset. Code is available at https://github.com/geovsion/SLEM .

This article concerns an optimal crowd motion control problem in which the crowd features a structure given by its organization into N groups (participants) each one spatially confined in a set. The overall optimal control problem consists in driving the ensemble of sets as close as possible to a given point (the 'exit') while the population in each set minimizes its control effort subject to its sweeping dynamics with a controlled state dependent velocity drift. In order to capture the conflict between the goal of the overall population and those of the various groups, the problem is cast as a bilevel optimization framework. A key challenge of this problem consists in bringing together two quite different paradigms: bilevel programming and sweeping dynamics with a controlled drift. Necessary conditions of optimality in the form of a Maximum Principle of Pontryagin in the Gamkrelidze framework are derived. These conditions are then used to solve a simple illustrative example with two participants, emphasizing the interaction between them., 6 pages

The enhanced phase-locked loop (EPLL) is one of the most famous PLLs in single-phase applications and a versatile tool for different signal processing applications, especially for the grid synchronization of power converters. Recently, it has been proved that the EPLL has a quite narrow stable zone (compared to its unstable zone) in the positive parameter space from a small-signal point of view. This zone will be even more narrow if maintaining a minimum stability margin is required. This article aims to modify the EPLL structure to improve its stability margin, and at the same time, make it unconditionally stable in the positive parameter space from a small-signal point of view.

The next big thing in smartphone development will be under-display cameras. Conventional color cameras, implemented under-display, starting to appear for consumer products. For secure face-authentication, additional information in the form of the third dimension is required. The time-of-flight (ToF) measurement principle is based on measuring the phase and consequently the delay of emitted and reflected light pulses. Unwanted reflections of the display hinder the implementation of under-display ToF cameras. These reflections introduce errors and circumvent the processing of the depth information. In this paper, we present novel measurement principles enabling under-display Time-of-Flight imaging. In contrast to traditional continuous wave ToF imaging, our measurement principles remove the the display introduced stray-light reflection from the captured images. We present two independent approaches. One is based on the combination of continuous wave measurement with coded modulation. The second method is based on measurements with two modulation frequencies. These methods require a non-traditional approach for depth and amplitude calculation. Furthermore, design considerations are presented, such as calibration and temperature dependency. Our extensive evaluation considers the reflection suppression performance and the fidelity of the captured depth data. The results reveal the suppression of the stray-light by more than 80%. We evaluate our methods with a detailed description of the advantages and disadvantages of the different methods. This work is a starting point for the implementation of under-display ToF imaging.

When forecasting with economic time series data, researchers often use a restricted window of observations or downweight past observations in order to mitigate the potential effects of parameter instability. In this paper, we study the problem of selecting a window for point forecasts made at the end of the sample. We develop asymptotic approximations to the sampling properties of window selection methods, and post-window selection point forecasts, where there is local parameter instability of various sorts. We examine risk properties of point forecasts made after cross-validation to select the window, and compare this approach to some alternative methods of selecting the window. We also propose a quasi-Bayesian form of cross-validation that we find to have good risk properties.

The maximum power point tracking (MPPT) is an essential function of the PV power system to achieve the maximum PV power utilization. Traditionally, the MPPT can be realized by a duty cycle modulated dc–dc converter. However, if the LLC resonant converter is considered for the PV power system, the frequency-modulated MPPT algorithm should be adopted. Several strategies such as the frequency-based perturbation and observation (P&O) method and the center point iteration (CPI) method were proposed for the LLC resonant converter. However, with the conventional P&O method, there will be a tradeoff between the tracking speed and the oscillation phenomenon. Besides, the CPI method is unable to maintain the maximum power output under varying environmental conditions. In view of this, an optimal frequency-modulated hybrid MPPT algorithm is proposed in this article. First, the proposed frequency-modulated impedance-matching (FMIM) method is developed to track an optimal operation point under present environmental conditions. Then, the P&O concept will be utilized to ensure maximum power harvesting from the PV array. On the other hand, the operation point calculated via the FMIM is very close to the real MPP. Therefore, the perturbation amount of the P&O method can be minimized to avoid the oscillation around the maximum power point. Operational principles and thorough mathematical derivations are also developed. Finally, both simulation results and hardware verifications obtained from a 500-W prototype circuit demonstrate the performance and feasibility of the proposed MPPT.

Aircraft detection in synthetic aperture radar (SAR) images plays a significant role in dynamic monitoring and national security. Previous methods have difficulty in obtaining the desirable detection performance due to the interference of complex scenes and diversity of aircraft sizes. In order to solve these problems, we propose an innovative scattering feature relation network (SFR-Net) in this paper. First, considering that the strong scattering points of the aircraft in SAR images are usually discrete, we leverage the proposed scattering point relation module to fulfill the analysis and correlation of scattering points. By enhancing the characteristics and relationships among the scattering points, this method is beneficial to guarantee the completeness of aircraft detection results. Second, we design a salient fusion module to adaptively aggregate the features from different layers of SFR-Net with rich semantic information and plentiful details, which can highlight the significant objects with different sizes and enhance the distinguishable features. Third, to reduce the false alarm and improve the localization accuracy, the contextual feature attention is presented to capture the global spatial and semantic information with a large receptive field. Overall, the SFR-Net is designed based on the SAR imaging mechanism and the scattering characteristics of aircrafts. The extensive experiments are conducted on the SAR aircraft detection dataset (AIRD) from the Gaofen-3 satellite to demonstrate the effectiveness of the SFR-Net and also illustrate that our method achieves the state-of-the-art performance.

The triangular libration points in the Earth-Moon system are considered to be potential candidates for future space missions because of their unique positions and dynamics. This paper presents a comprehensive transfer trajectory design and optimization framework for transfers from the Earth to L 4 in the Earth-Moon system, including direct transfer and transfer utilizing powered lunar gravity assist. In this framework, first, a synthetic procedure combining an initial guess, a differential correction algorithm and numerical continuation for generating tangential transfer families is illustrated. Subsequently, the optimization models for optimal transfer trajectories with the minimum velocity increment requirement are constructed. For direct transfer, the optimal direct transfer trajectory is obtained using primer vector theory. For the optimal transfer utilizing powered lunar gravity assist under a minimum swing-by height constraint, the necessary optimality conditions are found through optimal control theory, and an optimization procedure that allows for numerical convergence to the optimal solution is constructed. Numerical results indicate that the proposed transfer trajectory design and optimization framework has sufficient effectiveness to provide various transfer trajectories associated with different insertion points and fuel-optimal transfers.

In the work, we present a novel multiviewpoint and multitemporal remote sensing image registration method based on a dual-features Student-t distribution mixture model (DSMM) under a variational Bayesian (VB) framework. The main contributions of the work are: 1) guided image filter (GIF) is adopted to smooth edges and strengthen ridges of images for heightening characteristics of feature point; 2) a Student-t distribution mixture model (SMM) based DSMM designs a global and local descriptor to estimate correspondences from local to global scale; and 3) local structure constraints are designed to preserve relationships of neighbors of points and the scale of neighborhood structure of points to constrain transformation. The experimental results demonstrate the better performance of our DSMM against five state-of-the-art methods.

Unmanned aerial vehicle (UAV) hyperspectral imaging has been extensively applied in various fields. However, due to the limited imaging width, hyperspectral images (HSIs) captured by UAV need to be stitched so as to effectively cover the study area. In this paper, an effective seamless stitching method with deep feature matching and elastic warp is proposed for HSIs, which consists of the following major steps. First, for each input HSI, a single band grayscale image is obtained by fusing the bands corresponding to the red, green, and blue wavelengths. Second, the feature points of each HSI are obtained with a robust VGG-style network, and matched with a graph neural network. After point pairs are obtained, the next step is to estimate the transformation matrix of adjacent images, and a spectral correction method based on intrinsic decomposition is proposed to ensure the spectral consistency of adjacent images. In the final stage, a seam-cutting and multi-scale blending strategy is adopted to ensure the spatial consistency of the stitching results. Experimental results on real HSIs show that the proposed method is superior to six representative image stitching approaches.

Ultrawideband (UWB) radar can achieve ultrahigh-resolution inverse synthetic aperture radar (ISAR) imaging of noncooperative targets by transmitting UWB signals. However, the spatial-variant (SV) high-order migration through range cell (MTRC) and phase errors produced by the UWB radar system have seriously challenged the feasibility of conventional ISAR imaging algorithms. Moreover, maneuvering targets has exacerbated this problem compared with the steady ones. In this article, a UWB ISAR imaging algorithm of maneuvering targets with joint high-order motion compensation and azimuth scaling (JHOMCAS) is proposed. For the azimuth SV linear MTRC and 2-D SV high-order MTRC caused by the maneuvering rotational motion of the targets, the cascaded generalized keystone transform (GKT) is adopted for precise correction. It is worth noting that, when eliminating the SV MTRC by the cascaded GKT, the 2-D SV high-order phase errors induced by the maneuvering rotational motion must be accurately compensated, or MTRC correction will fail. The traditional autofocus methods usually only address the phase errors shared by the total target without due attention to the fine SV property. In response to this problem, this article first develops a joint 2-D SV autofocus and azimuth scaling algorithm (JSVAAS) to achieve the integration of SV high-order phase error compensation and azimuth scaling. A JHOMCAS algorithm is proposed to perform the joint processing of GKT and JSVAAS, ``GKT-JSVAAS-GKT.'' This approach helps accomplish the high-precision UWB ISAR imaging of maneuvering targets, and the well-focused and scaled UWB ISAR images obtained will build a sound foundation for target classification and recognition. Extensive experiments based on both scattering point simulation data and electromagnetic calculation data verify that the proposed algorithm outperforms conventional ISAR imaging approaches in UWB ISAR imaging of maneuvering targets.

In a recent paper published in this very journal, the “Bounded fuzzy possibilistic method” (BFPM) was proposed. We point out that there are some critical flaws in the said algorithm, which makes the results presented therein highly questionable. In particular, the method does not generate meaningful cluster membership degrees and fails to converge when run on some well-known benchmark data sets.

This work addresses the Nash equilibrium seeking problem in continuous potential games in a situation that the payoff functions and actions of players are blind to each other due to privacy security or communication limitation. To this end, a payoff-based learning dynamics is proposed with the limitation that only available information for each agent is its own past actions and several observed payoffs. In detail, we formulate a trial-and-error learning protocol to search for proper moving direction and length for action adjustment of each agent, and prove that following the proposed learning dynamics, the action profile of agents will be guaranteed to converge to a Nash equilibrium of the continuous potential game. For illustration of the theoretical development, the proposed payoff-based learning dynamics is further utilized to design an absolute distance-based consensus protocol for multi-agent systems. It is shown that agents can eventually reach a consensus point even when they do not know the relative positions with other agents.

This article introduces voltage-balancing control methods in stacked multicell converters based on single space-vector modulation. Two different control strategies for capacitors voltage are presented. The basic vectors with the smallest common-mode voltage are chosen to accomplish the voltage-balancing control. The nearest vector is selected for the first proposed method, and the appropriate switch combinations of each phase are determined by the cost function of floating-capacitor voltages and neutral point (NP) voltage. For the second proposed method, we consider sacrificing a little current performance to provide more possibility of switching state selection. The switching state of each phase is determined according to the voltage-balancing control of floating capacitors, and the optimal switching state with the smallest NP voltage is selected from the two nearest adjacent basic vectors. Experimental results compared with the existing space vector pulsewidth modulation method are presented to verify the effectiveness of the proposed methods.

In this paper, a novel robotic grasping system is established to automatically pick up objects in cluttered scenes. A composite robotic hand composed of a suction cup and a gripper is designed for grasping the object stably. The suction cup is used for lifting the object from the clutter first and the gripper for grasping the object accordingly. We utilize the affordance map to provide pixel-wise lifting point candidates for the suction cup. To obtain a good affordance map, the active exploration mechanism is introduced to the system. An effective metric is designed to calculate the reward for the current affordance map, and a deep Q-Network (DQN) is employed to guide the robotic hand to actively explore the environment until the generated affordance map is suitable for grasping. Experimental results have demonstrated that the proposed robotic grasping system is able to greatly increase the success rate of the robotic grasping in cluttered scenes., Comment: has been accepted by IEEE/RSJ International Conference on Intelligent Robots and Systems 2019

The Big Match is a multistage two-player game. In each stage, player 1 hides one or two pebbles in his hand, and his opponent has to guess that number. Player 1 loses a point if player 2 is correct; otherwise, he wins a point. As soon as player 1 hides one pebble, the players cannot change their choices in any future stage. The undiscounted Big Match has been much-studied. Blackwell and Ferguson (1968) give an [Formula: see text]-optimal strategy for player 1 that hides, in each stage, one pebble with a probability that depends on the entire past history. Any strategy that depends on just the clock or just a finite memory is worthless (i.e., cannot guarantee strictly more than the least reward). The long-standing natural open problem has been whether every strategy that depends on just the clock and a finite memory is worthless. The present paper proves that there is such a strategy that is [Formula: see text]-optimal. In fact, we show that just two states of memory are sufficient.

Change point detection plays a fundamental role in many real-world applications, where the goal is to analyze and monitor the behaviour of a data stream. In this paper, we study change detection in binary streams. To this end, we use a likelihood ratio between two models as a measure for indicating change. The first model is a single bernoulli variable while the second model divides the stored data in two segments, and models each segment with its own bernoulli variable. Finding the optimal split can be done in \( \mathcal {O} \mathopen {}\left( n\right) \) time, where n is the number of entries since the last change point. This is too expensive for large n. To combat this we propose an approximation scheme that yields \((1 - \epsilon )\) approximation in \( \mathcal {O} \mathopen {}\left( \epsilon ^{-1} \log ^2 n\right) \) time. The speed-up consists of several steps: First we reduce the number of possible candidates by adopting a known result from segmentation problems. We then show that for fixed bernoulli parameters we can find the optimal change point in logarithmic time. Finally, we show how to construct a candidate list of size \( \mathcal {O} \mathopen {}\left( \epsilon ^{-1} \log n\right) \) for model parameters. We demonstrate empirically the approximation quality and the running time of our algorithm, showing that we can gain a significant speed-up with a minimal average loss in optimality.

An overview of the current and future trends in wind turbine blade structural design process is presented. The main design principles and failure mechanisms of blades in operation are assessed and explained through an industry point of view, in a realistic manner. A number of failure modes which are not addressed sufficiently in the certificate guidelines are presented. An example on how to use the new design philosophy is presented. The manufactured prototype is a 44m long load carrying spar and the weight is reduced by 40%.

This paper presents a set of adaptive reset techniques for use with haptic retargeting systems focusing on interaction with hybrid virtual reality interfaces that align with a physical interface. Haptic retargeting between changing physical and virtual targets requires a reset where the physical and virtual hand positions are re-aligned. We present a modified Point technique to guide the user in the direction of their next interaction such that the remaining distance to the target is minimized upon completion of the reset. This, along with techniques drawn from existing work are further modified to consider the angular and translational gain of each redirection and identify the optimal position for the reset to take place. When the angular and translational gain is within an acceptable range, the reset can be entirely omitted. This enables continuous retargeting between targets removing interruptions from a sequence of retargeted interactions. These techniques were evaluated in a user study which showed that adaptive reset techniques can provide a significant decrease in task completion time, travel distance, and the number of user errors Refereed/Peer-reviewed

Forecast reconciliation is a post-forecasting process aimed to improve the quality of the base forecasts for a system of hierarchical/grouped time series (Hyndman et al., 2011). Contemporaneous (cross-sectional) and temporal hierarchies have been considered in the literature, but - except for Kourentzes and Athanasopoulos (2019) - generally these two features have not been fully considered together. Adopting a notation able to simultaneously deal with both forecast reconciliation dimensions, the paper shows two new results: (i) an iterative cross-temporal forecast reconciliation procedure which extends, and overcomes some weaknesses of, the two-step procedure by Kourentzes and Athanasopoulos (2019), and (ii) the closed-form expression of the optimal (in least squares sense) point forecasts which fulfill both contemporaneous and temporal constraints. The feasibility of the proposed procedures, along with first evaluations of their performance as compared to the most performing `single dimension' (either cross-sectional or temporal) forecast reconciliation procedures, is studied through a forecasting experiment on the 95 quarterly time series of the Australian GDP from Income and Expenditure sides considered by Athanasopoulos et al. (2019)., Main text: 49 pages, 10 figures, 2 tables. Appendix: 68 pages, 29 figures, 17 tables

In this paper, we investigate the movement of an inverted pendulum, the suspension point of which performs high-frequency oscillations along a line making a small angle with the vertical. We prove that under certain conditions on the function describing the oscillations of the suspension point of the pendulum, a periodic motion of the pendulum arises, and it is asymptotically stable.

A topological descriptor is a mathematical illustration of a molecular construction that relates particular physicochemical properties of primary molecular structure as well as its mathematical depiction. Topological co-indices are usually applied for quantitative structure action relationships (QSAR) and quantitative structure property relationships (QSPR). Topological co-indices are topological descriptor which consider the noncontiguous vertex set. In this study, we studyied some of the accompanied renowned topological co-indices: the 1st and 2nd Zagreb co-indices, the 1st and 2nd multiplicative Zagreb co-indices, and the F-coindex. By applying structure based examinations and deductions, we discuss the earlier stated co-indices of few synthetic atomic structures that are frequently used in clinical, synthetic, and material designing

In order to improve the accuracy of position and direction of the tool axis vector and the controlling accuracy of cutter contacting (CC) paths between the cutter and workpiece in the traditional five-axis NURBS interpolation method, a five-axis Tri-NURBS spline interpolation method is proposed in this paper. Firstly, the spline interpolation instruction format is proposed, which includes three spline curves, the CC point spline, the tool center point spline, and the tool axis point spline. The next interpolation parameter is calculated based on the tool center point spline combined with the conventional parametric interpolation method. Different from the traditional spline interpolation using the same interpolation parameter for all spline curves, the idea of equal ratio configuration of parameters is proposed in this paper to obtain the next interpolation parameter of each spline curve. The next interpolation tool center point, tool axis point, and CC point on the above three spline curves can be obtained by using different interpolation parameters, so as to improve the accuracy of position and direction of the tool axis vector. Secondly, the producing mechanism of the nonlinear error of CC paths of the traditional spline interpolation is analyzed and the mathematical calculation model of the nonlinear error is established. And then, the compensation and correction method of nonlinear error is also proposed to improve the controlling accuracy of CC paths. In this method, the next CC point on the cutter can be first obtained according to the next interpolation tool center point, tool axis point, and CC point on the three spline curves. And then, the error compensation vector is determined with the two next CC points. To correct the nonlinear error between the next CC point on the cutter and the CC point spline curve, the cutter is translated so that the two next CC points can coincide. In the end, the new tool center point and tool axis point after translation can be calculated to obtain the motion control coordinates of each axis of the machine tool. The MATLAB and VERICUT software are used as a simulation of the real machining data. The results show that the proposed method can effectively reduce the nonlinear error of CC paths. It has high practical value for five-axis machining in effectively controlling the accuracy of CC paths and improving the machining accuracy of complex surfaces.

Multimodal multi-objective problems (MMOPs) arise frequently in the real world, in which multiple Pareto optimal solution (PS) sets correspond to the same point on the Pareto front. Traditional multi-objective evolutionary algorithms (MOEAs) show poor performance in solving MMOPs due to a lack of diversity maintenance in the decision space. Thus, recently, many multimodal multi-objective evolutionary algorithms (MMEAs) have been proposed. However, for most existing MMEAs, the convergence performance in the objective space does not meet expectations. In addition, many of them cannot always obtain all equivalent Pareto solution sets. To address these issues, this study proposes a multimodal multi-objective evolutionary algorithm based on a weighted indicator, termed MMEA-WI. The algorithm integrates the diversity information of solutions in the decision space into an objective space performance indicator to maintain the diversity in the decision space and introduces a convergence archive to ensure a more effective approximation of the Pareto optimal front (PF). These strategies can readily be applied to other indicator-based MOEAs. The experimental results show that MMEA-WI outperforms some state-of-the-art MMEAs on chosen benchmark problems in terms of inverted generational distance (IGD) and IGD in the decision space (IGDX) metrics.

Steel Stud Wall Systems (SSWSs) have been attractive structural alternative due to its merits. This research is mainly focused on enhancing static resistance of SSWSs to resist moderate blast events. Resistance function is considered an important item of data in calculating dynamic response of a system using Single Degree of Freedom model. Resistance function can be obtained by quasi-static experimental loading. Or alternatively, using FE modelling for finding the non-linear static response of a system. This research shows how combinations of the various in-lack parameters can be arranged to maximize efficiency of the system for performance as blast proof structures. This is performed by analyzing different section geometries from traditional ones with aids of FE software (ABAQUS 16). The solution is under quasi-static loading and the obtained toughness is used to hold comparison between the new geometries and the traditional stud section. Two validation models followed by a comprehensive parametric study are performed to investigate back-to-back cold formed sections efficiency in comparison to traditional system from blast point of view. For this purpose, two new introduced factors Material Utilization Factor (M.U.F) and (EF) are used to relate the various design variables and outputs to the amount of material used in the sections. The paper shows how using the new sections and changing parameters may maximize the system efficiency to raise toughness and elastic load limit up to 3.94 times and 3.78 respectively from the traditional system. Finally, important conclusions are withdrawn about the efficiency of the new sections. Withdrawn conclusions are emphasizing that using back-to-back cold formed sections with a certain arrangement is much beneficious than traditional used sections.

Based on two-grid discretization and domain decomposition approach, this paper presents and studies two local and parallel finite element algorithms for the 2D/3D steady Stokes equations with nonlinear damping term. Starting point of the algorithms is that for a solution of the Stokes equations with damping, we first approximate the low-frequency component on a relatively coarse grid, and then compute the high-frequency component on a locally fine grid via some local and parallel procedures. The proposed algorithms are easy to implement and have low communication complexity. With the use of the technical tool of local a priori estimate for finite element solution, we derive error estimates of the approximate solutions from the proposed algorithms. Some numerical results are provided to test the validity of the present algorithms.

In this paper, we propose one novel progressive point cloud upsampling framework to tackle the non-uniform distribution issue during the point cloud upsampling process. Specifically, we design an Up-UNet feature expansion module which is capable of learning the local and global point features via a down-feature operator and an up-feature operator, respectively, to alleviate the non-uniform distribution issue and remove the outliers. Moreover, we design a hybrid loss function considering both the multi-scale reconstruction loss and the rendering loss. The multi-scale reconstruction loss enables each upsampling module to generate a denser point cloud, while the rendering loss via point-based differentiable rendering ensures that the proposed model preserves the point cloud structures. Extensive experimental results demonstrate that our proposed model achieves state-of-the-art performance in terms of both qualitative and quantitative evaluations.

Combined-permutation representations (CPRs) for characterizing -skeletons (a benzene skeleton, a Haworth-projected skeleton, a superphane skeleton, and a coronene skeleton) are constructed by starting from respective sets of generators, where the permutation of each generator is combined with a mirror-permutation of 2-cycle to treat both achiral and chiral substituents under the GAP system. Thereby, the CPR of degree 8 (= 6 + 2) for the benzene skeleton, the CPR of degree 14 (= 12 + 2) for the Haworth-projected skeleton, the CPR of degree 14 (= 12 + 2) for the superphane skeleton, the CPR of degree 14 (= 12 + 2) for the coronene skeleton are generated to give primary mark tables (tables of marks) based on these CPRs. These primary mark tables generated by the GAP system are different in the sequence of subgroups from each other, although they stem from the same point group . They are unified into a single standard mark table by means of a newly-devised GAP function MarkTableforUSCI. Moreover, another newly-devised GAP function constructUSCITable is employed to construct a standard USCI-CF (unit-subduced-cycle-index-with-chirality-fittingness) table concordantly. After a set of PCI-CFs (partial cycle indices with chirality fittingness) is calculated for each skeleton, symmetry-itemized combinatorial enumeration is conducted by means of the PCI method of Fujita’s USCI approach (S. Fujita, Symmetry and Combinatorial Enumeration in Chemistry, Springer-Verlag, Berlin-Heidelberg, 1991).

This article proposes a hybrid dual modulation waveform pulsewidth modulation (DMW-PWM) strategy for 5L-SMC. The driver signals of each cell are generated by comparing dual modulating waves and two phase-shifted carriers, respectively. The voltage natural-balancing characteristic of both the floating capacitors and dc-link capacitors with the proposed DMW-PWM is derived. The proper active voltage balance control strategy is also presented to regulate the voltages of neutral point and floating capacitors. With this strategy, the voltage balancing of dc-link capacitors and floating capacitors is achieved simultaneously with the complete elimination of low-frequency ripples in neutral point. Compared with the conventional DMW-PWM method (also called the fast processing modulation method), the proposed method has a smaller voltage fluctuation in floating capacitors, better dynamic performance, lower switching frequency, and simply digital implementation. The superiority of the proposed method is verified by experimental results.

Molecular communication often involves coexisting links where certain links may have priority over others. In this work, we consider a system in three-dimensional (3-D) space with two coexisting communication links, each between a point transmitter and fully-absorbing spherical receiver (FAR), where the one link (termed primary) has priority over the second link (termed secondary). The system implements the underlay cognitive-communication strategy for the co-existence of both links, which use the same type of molecules for information transfer. Mutual influence of FARs existing in the same communication medium results in competition for capturing the information-carrying molecules. In this work, first, we derive an approximate hitting probability equation for a diffusion-limited molecular communication system with two spherical FARs of different sizes considering the effect of molecular degradation. The derived equation is then used for the performance analysis of primary and secondary links in a cognitive molecular communication scenario. We show that the simple transmit control strategy at the secondary transmitter can improve the performance of the overall system. We study the influence of molecular degradation and decision threshold on the system performance. We also show that the systems parameters need to be carefully set to improve the performance of the system.

There have been attempts to improve the operational efficiency of construction projects and plan response countermeasures for estimated damage following disasters through the utilization of accumulated electronic data, which constructs a digital twin that can reproduce a physical space in cyberspace and feedback the cyberspace simulation results to physical space. However, the application of such simulations is limited, unless numerical models can be automatically constructed from the data. In this study, we develop a program that utilizes a data processing platform to read, transform, and integrate data to create mediated data with a common data structure. This mediated data can be used to construct analytical models for various numerical analyses. Using the program developed, a grid model of 3-D ground surface as the mediated data was constructed based on borehole data obtained through a ground survey. Each grid point has basic material parameters of soil, and these parameters are estimated from borehole data and other investigation reports. Each grid point has general geotechnical parameters and can be easily converted to a 3-D finite-element model. When the borehole data is added or changed, the analytical model can be updated with almost no cost, whereas it would be very costly to create the model manually.