Dynamic Point Research Articles

DeepPCT: Single Object Tracking in Dynamic Point Cloud Sequences

Three-dimensional single object tracking (SOT) in dynamic point cloud sequences is a fundamental problem for numerous intelligent systems. In this article, we propose a deep neural architecture named DeepPCT to achieve accurate and robust tracking in sequential point clouds. Instead of relying on the bounding box classification and regression to localize the object, the proposed framework directly formulates the intersection-over-union (IoU) prediction as its objectives for accurate 3-D localization. A Center IoU (CIoU) is further proposed to mitigate the ambiguity of standard IoU. In addition, we also investigate the impact of an additional re-detection module to the overall tracking performance, since tracking failures frequently occur due to the large variations and error accumulation. Experimental results on the KITTI benchmark demonstrate that the proposed tracker achieves competitive performance, with a 3-D precision rate of 63.7% and a 3-D success rate of 45.0%. Moreover, our tracker achieves compelling performance in the wild with severe illumination variations and occlusions, and even outperforms several 2-D counterpart trackers.

Non-liner elastic and thermal properties of semiconductor Aluminum Arsenide (AlAs)

The study of non-linear elasticity and elastic anisotropy in semiconducting crystals is a subject of great interest. Murnaghan’s finite deformation theory and elastic theory of Thurston and Brugger are used to study the elasticity in Aluminum Arsenide (AlAs). Elastic properties have direct link with specific heat, thermal expansion and Gruneisen parameters. The anharmonicity of Aluminum Arsenide (AlAs) crystal lattice are evaluated using the higher order elastic constants. Theoretical calculations from the thermodynamic and lattice dynamic points are useful in the study of the elastic and thermal properties of the semiconductor. The objective of this study is to investigate the higher order elastic constants, the low temperature limit of thermal expansion and the Gruneisen parameters of AlAs.

CenterTube: Tracking Multiple 3D Objects With 4D Tubelets in Dynamic Point Clouds

3D Multi-Object Tracking (MOT) in dynamic point cloud sequences is a fundamental research problem for several downstream tasks such as motion planning and action recognition. Existing methods usually rely on the traditional tracking-by-detection (TBD) paradigm, which performs the tracking based on the results achieved by dedicated detectors. However, this two-stage framework usually cannot sufficiently exploit spatial-temporal information and end-to-end optimization, leading to sub-optimal tracking performance, especially when the object is partially or completely occluded. In this paper, we propose a joint detection and tracking framework named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CenterTube</i> for dynamic point cloud sequences. The key to our approach is to formulate the problem of multiple object trajectory predictions as 4D tubelet detections. In particular, the proposed CenterTube is composed of three head branches, including a center branch, a regression branch, and a movement branch for the estimation of object center, object size, instance movement, and frame interval, respectively. Additionally, a Tube BEV-IoU (TB-IoU) is also presented to link the generated clip-level tubelets and form the final tracks. Extensive experiments conducted on the KITTI-MOT and nuScenes datasets demonstrate that our model achieves competitive performances even if no ready-made detection results is adopted.

Motion-Compensated Predictive RAHT for Dynamic Point Clouds.

We study the use of predictive approaches alongside the region-adaptive hierarchical transform (RAHT) in attribute compression of dynamic point clouds. The use of intra-frame prediction with RAHT was shown to improve attribute compression performance over pure RAHT and represents the state-of-the-art in attribute compression of point clouds, being part of MPEG's geometry-based test model. We studied a combination of inter-frame and intra-frame prediction for RAHT for the compression of dynamic point clouds. An adaptive zero-motion-vector (ZMV) scheme and an adaptive motion-compensated scheme are developed. The simple adaptive ZMV approach is able to achieve sizable gains over pure RAHT and over the intra-frame predictive RAHT (I-RAHT) for point clouds with little or no motion while ensuring similar compression performance to I-RAHT for point clouds with intense motion. The motion-compensated approach, more complex and more powerful, is able to achieve large gains across all of the tested dynamic point clouds.

GMP-SLAM: A real-time RGB-D SLAM in Dynamic Environments using GPU Dynamic Points Detection Method

Simultaneous Localization and Mapping (SLAM) is a fundamental technology for robotics. Vision-based SLAM has been developed for many years, but it is still difficult for SLAM system to handle dynamic environments. In this paper, we present GMP-SLAM, a realtime RGB-D SLAM system for highly dynamic environments with the help of GPU Grid Map Projection— a GPU dynamic points detection method we design. SLAM is a time-sensitive system for robotics, and it is hard to reach real time, especially in dynamic environments because it is necessary to track moving objects and it takes a lot of time. GMP-SLAM is based on ORB-SLAM2, which is one of the best feature-based SLAM frameworks and can reach real time just in CPU. But ORB-SLAM2 cannot handle highly dynamic environments very well, and most work focus on tracking moving objects with a neural network, which cannot reach real time even with the help of GPU. To solve real-time problem, we propose an all-in-parallel dynamic points detection framework for visual simultaneous localization and mapping (VSLAM) in dynamic environments based on 3D occupancy grid maps. Our SLAM system can provide not only higher trajectory accuracy but also a 3D grid map for navigation. We test our SLAM system in our real-world datasets we record and get higher trajectory accuracy than ORB-SLAM2. At the same time, our system can run nearly in 20Hz, which is much better than existing VSLAM framework in dynamic environments.

Lithium-ion battery remaining useful life prediction using a two-phase degradation model with a dynamic change point

An accurate remaining useful life (RUL) prediction plays a crucial role in the prognostics and health management of lithium-ion (Li-ion) batteries. Current studies on the RUL prediction of Li-ion batteries commonly use single-phase degradation models, which result in inaccurate RUL predictions due to their insufficient capabilities in capturing various degradation patterns. The existing two-phase degradation models can divide battery degradation into two phases using a change point, a slowly decreasing phase, and a rapidly decreasing phase. The change point in the current two-phase degradation models is usually modeled in two ways. First, the change point is treated as a random variable and that however greatly increases the computational complexity. Second, a fixed change point is assigned for all battery cells for model simplification, which may not be realistic in practice. For example, battery cells' degradation data collected from our laboratory tests show a two-phase degradation pattern with different change points. By considering such differences in change points, this study first utilizes binary segmentation to identify the change point of a battery cell and then proposes a two-phase capacity degradation model with a dynamic change point. Further, variations have been observed in the degradation behaviors of tested battery cells. Therefore, by using the proposed two-phase degradation model, we develop a particle filtering-based framework considering uncertainties to predict the RULs of battery cells. Finally, the proposed framework shows superior prediction performance compared with the existing degradation models by providing the RUL prediction with an average absolute estimation error percentage of 27 % for laboratory data and an average absolute estimation error percentage of 24 % for NASA battery data.

Guidance Law for Autonomous Takeoff and Landing of Unmanned Helicopter on Mobile Platform Based on Asymmetric Tracking Differentiator

For some flight missions, such as autonomous landing on mobile platforms, the demands on indicators such as target-tracking accuracy and so on are relatively high. To achieve this, a guidance system with excellent precision is necessary. An asymmetric tracking differentiator based on a tracking differentiator is proposed to establish the guidance system. On the basis of the proposed asymmetric tracking differentiator, an altitudinal and horizontal helicopter guidance system structure is designed. In this paper, a guidance law is designed in order to meet the accuracy and precision requirements in the autonomous landing and transition process. Apart from that, a plane-motion-guidance law is also designed to realize static and dynamic point tracking, linear route tracking and circular route tracking to improve the trajectory smoothness and accuracy. Finally, simulations of the autonomous landing process on moving platforms, including three stages, namely approaching, tracking and landing, are completed. The application effects and precision of the autonomous landing guidance algorithm under different wave heights and period conditions are analyzed through the obtained simulation curves.

Operational modal analysis of two typical UHV transmission towers: A comparative study by fast Bayesian FFT method

The ultra-high-voltage (UHV) transmission tower is currently the most advanced transmission tower in the world. The UHV transmission line is mainly composed of tangent towers and tension towers with different structural forms. This is interesting from a dynamic point of view, since the two typical structures may behave differently in terms of dynamic characteristics. In this study, a comparative study on operational modal analysis (OMA) of these two typical UHV transmission towers under ambient test is carried out by a fast Bayesian FFT method. Prior to the test, the selected frequency bands of modeling are determined by the sensitivity analysis. A structural health monitoring (SHM) system is installed on the towers during the maintenance period to monitor the acceleration response. The dynamic properties of the towers are obtained by the recently developed fast Bayesian FFT method and compared, the accuracy approximation of the Bayesian approach is verified by frequency domain information reconstruction. Furthermore, the damping ratio results as well as other voltage levels are compared with different nations codes. It is found that the stiffness of the tension tower is relatively higher than that of the tangent tower. The first-mode damping ratio results of tower in all directions exceeds 2.59% and the vertical cross-arm direction of tension tower reaches 4.54%. The research results can provide a reference for the wind resistance design for UHV transmission tower, and can be used in model updating in the future works.

Taking a chance on love: scenes, scripts, and affects in Four Saints in Three Acts and Jazz-Cabaret songs and performances of the 1930s

ABSTRACT Gertrude Stein and Virgil Thomson’s opera Four Saints in Three Acts (1934) famously combined avant-garde aesthetic practices and elements of popular culture. While commentators routinely note the recurrence of folk- and hymn-tune harmonies and cadences in Thomson’s otherwise resolutely ‘modernist’ music, they have accorded virtually no attention to the jazz-cabaret song, a form of popular music that emerged and established itself artistically and commercially during the same decade that Four Saints in Three Acts was produced and performed. This article examines a dynamic point of intersection between Stein and Thomson’s avant-garde opera and the jazz-cabaret song-form of the 1930s in some of the performances of Black cabaret star Jimmie Daniels as they were experienced by Thomson and other gay-male habitués of Harlem and Paris cabarets. One of Daniels’s signature tunes, the 1932 Harold Arlen-Ted Koehler song, ‘I’ve Got the World on a String’, is analysed in relation to Freud’s account of the fort-da ‘game’ and more generally in relation to Silvan Tomkins’s script theory.

The Effect of a Blast Load from Propane Gas Leakage on an RC Structure

The knowledge of the way a gas explosion affects a structure is still in its development. In this study, structural analysis software was used to construct a simplified approach to predict the impact of a Dynamic Blast Load (DBL) from gas leakage. During the explosion process, the overpressure distribution in a room is decomposed to maximum dynamic pressure point (DPPmax), where the explosion damages the most the structure. The simulation of the DBL was compared with the real consequences of gas explosion with the phenomenological data of a case study in Batna, Algeria. The results show that the simulation reproduced by our modeling of the DBL at structural scale is in good agreement with a real explosion. The distribution of stresses and strains over time indicates that the gas explosion inside residential buildings affected the entire surrounding of the area of the blast.

Virtual Dynamic Grid Impedance and Its Impacts on Harmonics and Stability of Inverter Based Resources Plant

Nowadays, a large number of inverter-based resources (IBRs) are integrated into the grid at a single connection point as an IBR plant. In this article, a virtual dynamic grid impedance concept is introduced to evaluate the harmonics and stability for grid integration of an IBR plant containing multiple IBRs. First, a detailed theoretical study is conducted to build a foundation of the virtual grid impedance concept, which is a dynamic impedance that changes with the number of IBRs added into an IBR plant. Then, based on the new virtual dynamic grid impedance concept, frequency spectrum analysis is performed to explore harmonic impact at the interconnection point for an IBR plant with IBRs having L, LC, and LCL filters, respectively. An electromagnetic transient (EMT) simulation model of a grid-connected IBR plant is developed to explore the harmonics and stability of the IBR plant connected to the grid as well as the reliable operation of IBRs within the plant from the novel virtual equivalent dynamic grid impedance point of view. Hardware experiments are conducted to validate the EMT simulation evaluation. The results show that the number of IBRs added into an IBR plant influences the grid impedance, i.e., grid strength, and the grid impedance variation has multiple impacts on the IBR plant and IBRs within the plant depending on the grid-connected filters of the IBRs.

Rapid Re-Convergence of Real-Time Dynamic Precise Point Positioning by Adding Velocity Constraints

Rapid Re-Convergence of Real-Time Dynamic Precise Point Positioning by Adding Velocity Constraints

Dynamic magnetic properties and phase diagrams of Fe4N system

The dynamic magnetic properties of the nonequilibrium Fe4N system are calculated on the basis of correlated effective-field theory (EFT). The dynamic phase diagrams are plotted for different values of the oscillating magnetic field and the crystal field. The reentrant behavior, the dynamic tricritical point and the dynamic critical end point observed in certain case by using EFT are established to emerge as the generic nonequilibrium features of the Fe4N system. Moreover, we compare our results with those produced by mean-field theory (MFT), and find that the first-order phase transition and the reentrant phenomena are significantly reduced due to the consideration of spin–spin thermal fluctuations in EFT approximations. Our findings lead us to conclude that the thermal fluctuations considered in EFT are a critical factor for the dynamic magnetic properties of the nonequilibrium Fe4N system.

Integrated structural FE analyses of MITICA plant

In the design of complex facilities and plants, such as those involved for fusion experiments, it is often very challenging to create detailed global structural models in the design phase, able to represent the structure as a whole, and at the same time to take into account the interaction among different components. Indeed, different components are characterized by a different scale and hence require a different level of detail.This work presents an integrated global FE model of the MITICA plant (Padua, IT) for the study of the behaviour and interaction of the structural elements, with particular attention to the final configuration after the installation.The model thus takes into account all the interactions between the various parts, initially designed in a decoupled way, with the aim of verifying, both from a static and dynamic point of view, the main elements and the interface forces between the components. Different constraint configurations have been taken into account according to the possible kinematic behaviour that the plant can assume, and depending also on the use of the facility.A single integrated global model allows also to identify the most critical elements of the structure, and hence to realize detailed sub-models for the local verification, and to eventually design a proper monitoring system.In conclusion, this work demonstrates the importance of having an integrated model, and proposes a strategy specifically tailored for the MITICA plant. This work lays also the foundation to develop Digital Twin system for fusion experiment plant.

LRD-SLAM: A Lightweight Robust Dynamic SLAM Method by Semantic Segmentation Network

With the development of intelligent concepts in various fields, research on driverless and intelligent industrial robots has increased. Vision-based simultaneous localization and mapping (SLAM) is a widely used technique. Most conventional visual SLAM algorithms are assumed to work in ideal static environments; however, such environments rarely exist in real life. Thus, it is important to develop visual SLAM algorithms that can determine their own positions and perceive the environment in real dynamic environments. This paper proposes a lightweight robust dynamic SLAM system based on a novel semantic segmentation network (LRD-SLAM). In the proposed system, a fast deep convolutional neural network (FNet) is implemented into ORB-SLAM2 as a semantic segmentation thread. In addition, a multiview geometry method is introduced, in which the accuracy of detecting dynamic points is further improved through the difference in parallax angle and depth, and the information of the keyframes is used to repair the static background information absent from the removal of dynamic objects, to facilitate the subsequent reconstruction of the point cloud map. Experimental results obtained using the TUM RGB-D dataset demonstrate that the proposed system improves the positioning accuracy and robustness of visual SLAM in indoor pedestrian dynamic environments.

A Monocular-Visual SLAM System with Semantic and Optical-Flow Fusion for Indoor Dynamic Environments.

A static environment is a prerequisite for the stable operation of most visual SLAM systems, which limits the practical use of most existing systems. The robustness and accuracy of visual SLAM systems in dynamic environments still face many complex challenges. Only relying on semantic information or geometric methods cannot filter out dynamic feature points well. Considering the problem of dynamic objects easily interfering with the localization accuracy of SLAM systems, this paper proposes a new monocular SLAM algorithm for use in dynamic environments. This improved algorithm combines semantic information and geometric methods to filter out dynamic feature points. Firstly, an adjusted Mask R-CNN removes prior highly dynamic objects. The remaining feature-point pairs are matched via the optical-flow method and a fundamental matrix is calculated using those matched feature-point pairs. Then, the environment's actual dynamic feature points are filtered out using the polar geometric constraint. The improved system can effectively filter out the feature points of dynamic targets. Finally, our experimental results on the TUM RGB-D and Bonn RGB-D Dynamic datasets showed that the proposed method could improve the pose estimation accuracy of a SLAM system in a dynamic environment, especially in the case of high indoor dynamics. The performance effect was better than that of the existing ORB-SLAM2. It also had a higher running speed than DynaSLAM, which is a similar dynamic visual SLAM algorithm.

Dynamic Magnetic Features of a Mixed‐Spin‐2 and Spin‐5/2 Ising Ferrimagnetic System under a Time‐Dependent Oscillating Magnetic Field: Path Probability Method Approach

A study, within the path probability method, of the dynamic magnetic features of the mixed spin (2, 5/2) Ising ferrimagnetic system on a hexagonal lattice under the presence of a sinusoidal external magnetic field is presented. The Hamiltonian of the system contains intersublattice, intrasublattice, and crystal‐field interactions. The intersublattice interaction is taken as antiferromagnetic. The time variations of average dynamic magnetizations and the thermal behavior of the dynamic magnetizations are investigated to obtain the phases in the system and to characterize the nature of the dynamic phase transitions and find the dynamic phase transition temperature points. The dynamic phase diagrams in six different phase planes are presented, in which they contain the paramagnetic (p) phase, three different ferrimagnetic (i 1, i 2, i 3) fundamentals, and the p + i 1, i 1 + i 2, and i 2 + i 3 mixed phases as well as the dynamic tricritical point and the dynamic double critical end point, depending on the interaction parameters. The dynamic compensation behavior is also examined and only N‐type behaviors are observed. Some of the obtained dynamic phase diagrams are in qualitatively good agreement within some works on the dynamics of mixed‐spin Ising system.

Visual SLAM Mapping Based on YOLOv5 in Dynamic Scenes

When building a map of a dynamic environment, simultaneous localization and mapping systems have problems such as poor robustness and inaccurate pose estimation. This paper proposes a new mapping method based on the ORB-SLAM2 algorithm combined with the YOLOv5 network. First, the YOLOv5 network of the tracing thread is used to detect dynamic objects of each frame, and to get keyframes with detection of dynamic information. Second, the dynamic objects of each image frame are detected using the YOLOv5 network, and the detected dynamic points are rejected. Finally, the global map is constructed using the keyframes after eliminating the highly dynamic objects. The test results using the TUM dataset show that when the map is constructed in a dynamic environment, compared with the ORB-SLAM2 algorithm, the absolute trajectory error of our algorithm is reduced by 97.8%, and the relative positional error is reduced by 59.7%. The average time consumed to track each image frame is improved by 94.7% compared to DynaSLAM. In terms of algorithmic real-time performance, this paper’s algorithm is significantly better than the comparable dynamic SLAM map-building algorithm DynaSLAM.

Comprehensive analysis on the dynamic characteristic of K-Rb-21Ne co-magnetometer with transient response

Spin-exchange relaxation-free (SERF) co-magnetometer is expected to be a high-precision gyroscope, but the slow response speed severely restricts its practicality. In this paper, a transient response analysis is proposed to give comprehensively quantify the dynamic characteristic of the SERF co-magnetometer. A compensation magnetic field configuration is proposed for the fastest response speed the system can achieve. The results show that the system gets the fastest response speed at the dynamic compensation point we proposed and the response speed of the system at that point is 19.36 times faster than that at the traditional magnetic compensation point. In addition, derived from experimental data, the spin-exchange relaxation induced by magnetic fields is observed and quantified, which decreases the sensitivity of inertia and magnetic measurement. This research supports improving the response speed of SERF co-magnetometer and expanding its application range, such as platform inertial navigation systems.

Research on travel path choice considering complex environment: theory, model and case

In this paper, considering the heterogeneity of travelers’ decision-making behavior caused by travel environment factors, thus affecting the choice of travel path, the theories and methods of travel path choice based on improved cumulative prospect theory (ICPT) in complex environment were proposed. On the basis of cumulative prospect theory (CPT), the value function was improved, and the parameter value range was enlarged. The nonlinear curve of value function and weight function of cumulative prospect theory was fitted through thousands of data tests and experiments. Then according to the decision preference, the decision makers were divided into different categories and the reference point value relationship of heterogeneous decision makers was found. In this paper, fuzzy travel time reference point and periodic dynamic risk degree reference point were set up, and a dynamic path selection model based on heterogeneous double reference point was established to improve the cumulative prospect value. Taking the highway network in Sichuan-Tibet region for example, the optimal path selection scheme of heterogeneous travel groups under the complex environmental factors such as debris flow and landslide in each time stage was studied, and the influence of preference parameter of travel time and risk degree on path choice was analyzed, and then the parameter sensitivity in the cumulative prospect theory (ICPT) was analyzed. The research results verified the rationality of the improved theory and method proposed in this study, which not only provide a new way of thinking for the study of travel path choice in complex environment but also provide theoretical guidance value for supporting regional traffic planning and construction in complex environment.