Dynamic Point Research Articles

Game Evolution Research on the Choice of Training Strategies between Clinical Departments and Doctors under the Background of Hospital-Provided Training

Objective: This study aims to explore the dynamic game process of training participation strategies among hospitals, clinical departments, and newly recruited doctors, as well as the impact of hospital intervention on this process. By applying game evolution theory, a comprehensive model is constructed to analyze the decision-making logic of training participation and propose new training strategies to promote the professional growth of newly recruited doctors and improve hospital service quality. Methods: The stability of dynamic game equilibrium points was analyzed by constructing Jacobian matrices, considering nine different game scenarios with and without hospital intervention. An empirical study from a hospital was introduced to verify the theoretical model's predictions. Results: The study found that without hospital intervention, clinical departments and newly recruited doctors tend to choose not to participate in training. After hospital intervention, through the provision of resources and incentives, the participation and effectiveness of training significantly improved. Conclusion: The study reveals the crucial role of hospital intervention in promoting the participation of newly recruited doctors in training and proposes a training strategy that combines theory and practice. This strategy not only helps new doctors quickly adapt to the hospital environment but also improves the quality of medical services, providing talent support for the hospital's continuous development. It offers theory-based decision support for hospital managers and provides new ideas for the improvement of future medical education and practice.

Trajectory tracking for autonomous surface ships using Gaussian process regression and model predictive control with BVS strategy

Autonomous navigation is critical to the development of next-generation shipping systems. The proposal of intelligent ships enables innovation in the shipping and shipbuilding industry and increases the safety and efficiency of ship operations. Autonomous control of surface ships to follow a prescribed trajectory is critical in a variety of maritime applications. This paper proposes a trajectory tracking control strategy for autonomous surface ships that combines nonparametric modelling using Gaussian Process Regression (GPR) with the Model Predictive Control (MPC) framework. A Bézier curve-based Virtual Ship(BVS) guidance strategy is proposed to convert dynamic trajectory points into reference heading angles and speeds, such that the trajectory tracking problem can be decomposed into heading control and speed control problems. Gaussian process regression is utilised to identify the correlation between propeller revolution speed and ship speed, as well as the correlation between rudder angle and heading angle based on experimental data. Two GPR models are therefore constructed as the prediction models for designing MPC controllers for heading control and speed control, respectively. Nonlinear optimisation algorithms are utilised to search for optimal control commands in each sampling interval to solve the optimisation problem in MPC with GPR models and input constraints. Simulations are carried out to evaluate the effectiveness of the proposed method.

A LiDAR SLAM Algorithm Considering Dynamic Extraction of Feature Points in Underground Coal Mine

Abstract. To address the absence of GNSS signals in underground coal mines and the susceptibility of mainstream LiDAR SLAM to degradation due to insufficient feature constraints, this paper proposes a tightly-coupled SLAM algorithm incorporating LiDAR and IMU for use in such environments. The paper initially introduces a dynamic feature point extraction strategy, where the number of corner feature points can be dynamically adjusted based on the occurrence of degradation in the underground coal mine environment. This approach constructs a constraint matrix with rich and robust feature information, enhancing pose estimation accuracy in environments with inadequate feature constraints, mitigating degradation effects, and reducing global cumulative error. Subsequently, the consistent building of the underground coal mine environment is achieved through back-end factor graph optimization. Finally, to validate the effectiveness of the method, experimental analysis is conducted in an underground coal mine. The results demonstrate that LeGO_LOAM exhibits poor robustness in underground coal mines and fails to construct a globally consistent pose estimation and map. Conversely, the pose estimation error of the proposed method in this paper is 50.93% lower in the plane direction and 42.13% lower in the elevation direction compared to LIO_SAM. This underscores the method's significance as crucial technical support for the intelligent perception and positioning of robots in underground coal mines.

Visual SLAM system based on dynamic semantic features

Aiming at the problem that dynamic objects (such as pedestrians, vehicles, and animals) in real scenes affect the positioning and mapping accuracy of visual SLAM (Simultaneous Localization and Mapping), a YOLOv3-ORB-SLAM3 algorithm is proposed based on ORB-SLAM3 (Oriented FAST and Rotated BRIEF-Simultaneous Localization and Mapping 3). The algorithm adds a semantic thread on the basis of ORB-SLAM3, and adopts a dual-thread mechanism for dynamic and static scene feature extraction: the semantic thread uses YOLOv3 to perform semantic recognition target detection on dynamic objects in the scene, and removes outliers from the extracted dynamic area feature points; the tracking thread extracts scene area features through ORB features, and obtains static scene features combined with semantic information and sends them to the backend, thereby eliminating the interference of dynamic scenes on the system and improving the positioning accuracy of the visual SLAM algorithm. Verified using the TUM (Technical University of Munich) dataset, the results show that the ATE (Average Treatment Effect) index of the YOLOv3-ORB-SLAM3 algorithm in the monocular mode is about 30% lower than that of the ORB-SLAM3 algorithm, and the ATE index of the dynamic sequence in the RGB-D (Red, Green and Blue-Depth) mode is reduced 10%, while the static sequence has no significant decrease.

ADS–SLAM: a semantic SLAM based on adaptive motion compensation and semantic information for dynamic environments

Abstract Static environment assumptions are a prerequisite for simultaneous localization and mapping (SLAM), while interference from dynamic objects in the environment can seriously impair the system’s localization accuracy. Recently, many works have combined deep learning and geometric constraints to attenuate the interference of dynamic objects, but poor real-time performance and low accuracy in high dynamic scenes still exist. In this paper, we propose a semantic SLAM algorithm for complex dynamic scenes named ADS–SLAM. Our system combines the advantages of semantic information and motion constraints to remove dynamic points during tracking and localization. First, an adaptive dynamic point detection method based on epipolar constraint between adjacent frames is designed to adapt to the changes of object motion states and a motion area detection method based on Gaussian mixture model and Kalman Filter is utilized to effectively compensate the missed motion areas. Second, an object detection network with improved inference in the backend is utilized to extract prior object semantics. Lastly, the multi-level information is integrated in order to comprehensively screen all dynamic points in the environment and utilize only static points for pose estimation and optimization. Experimental evaluations on challenging public datasets and outdoor dynamic environments demonstrate that our algorithm achieves high localization accuracy in almost all dynamic scenarios compared to the current state-of-the-art SLAM algorithms, with the highest accuracy in high dynamic scenarios, and shows real-time performance for practical applications.

Multi-view high-dynamic-range 3D reconstruction and point cloud quality evaluation based on dual-frame difference images

A high dynamic range can easily lead to image saturation, making it a challenge for structured light 3D reconstruction. The article proposes a multi-view 3D topography measurement system, which consists of dual projectors, a single camera, and a high-precision rotary platform. The system utilizes single-frame images to achieve high-dynamic-range surface adaptive exposure. Subsequently, it proposes a method that combines two-frame differential images with multi-view imaging to identify highly reflective regions and complete point cloud holes. This approach addresses the issue of visual blind spots caused by shadows and local high reflectivity due to the occlusion of the measured object’s geometric features. Finally, the system employs deep learning to evaluate the quality of the high-dynamic-range point cloud data. Comparative experiments show that optimal exposure in single-frame images can achieve better imaging quality and shorten capture time. The dual-frame difference image algorithm can identify high-reflection areas and complete the point cloud data. The point cloud quality evaluation model based on IT-PCQA demonstrates the effectiveness of the proposed method for high-dynamic-range 3D reconstruction.

YPR-SLAM: A SLAM System Combining Object Detection and Geometric Constraints for Dynamic Scenes

Traditional SLAM systems assume a static environment, but moving objects break this ideal assumption. In the real world, moving objects can greatly influence the precision of image matching and camera pose estimation. In order to solve these problems, the YPR-SLAM system is proposed. First of all, the system includes a lightweight YOLOv5 detection network for detecting both dynamic and static objects, which provides pre-dynamic object information to the SLAM system. Secondly, utilizing the prior information of dynamic targets and the depth image, a method of geometric constraint for removing motion feature points from the depth image is proposed. The Depth-PROSAC algorithm is used to differentiate the dynamic and static feature points so that dynamic feature points can be removed. At last, the dense cloud map is constructed by the static feature points. The YPR-SLAM system is an efficient combination of object detection and geometry constraint in a tightly coupled way, eliminating motion feature points and minimizing their adverse effects on SLAM systems. The performance of the YPR-SLAM was assessed on the public TUM RGB-D dataset, and it was found that YPR-SLAM was suitable for dynamic situations.

DGRO: Doppler Velocity and Gyroscope-Aided Radar Odometry

A stable and robust odometry system is essential for autonomous robot navigation. The 4D millimeter-wave radar, known for its resilience in harsh weather conditions, has attracted considerable attention. As the latest generation of FMCW radar, 4D millimeter-wave radar provides point clouds with both position and Doppler velocity information. However, the increased uncertainty and noise in 4D radar point clouds pose challenges that prevent the direct application of LiDAR-based SLAM algorithms. To address this, we propose a SLAM framework that fuses 4D radar data with gyroscope readings using graph optimization techniques. Initially, Doppler velocity is employed to estimate the radar’s ego velocity, with dynamic points being removed accordingly. Building on this, we introduce a pre-integration factor that combines ego-velocity and gyroscope data. Additionally, leveraging the stable RCS characteristics of radar, we design a corresponding point selection method based on normal direction and propose a scan-to-submap point cloud registration technique weighted by RCS intensity. Finally, we validate the reliability and localization accuracy of our framework using both our own dataset and the NTU dataset. Experimental results show that the proposed DGRO system outperforms traditional 4D radar odometry methods, especially in environments with slow speeds and fewer dynamic objects.

Effects of minimum monitor unit per dynamic control point on intensity‐modulated radiotherapy planning for nasopharyngeal carcinoma: A retrospective study

The present study aimed to improve the dose distribution of radiotherapy planning for nasopharyngeal carcinoma (NPC) by comparing the effects of various minimum monitor units (MUs) per dynamic control point (MMCP) values on the quality and execution efficiency of dynamic intensity-modulated radiotherapy (IMRT) planning. Thirty-four clinically implemented dynamic IMRT plans for patients with NPC were retrospectively selected. In total, 170 plans were obtained by modifying only the MMCP values (set as 1, 3, 5, 7, and 9) in the treatment planning system's (TPS) optimization parameters. These plans were divided into 5 groups. Analyzing the effects of MMCP on the target and organ dose at risk (OAR), also the execution efficiency of the treatment plan in each group and using a quality score system, we conducted an objective quantitative study of the dose distribution and execution efficiency. The target dose evaluation indicators (target coverage (TC), homogeneity index (HI), and conformity index (CI)) of all IMRT plans showed a trend of variation with an increase in MMCP values, and the difference was statistically significant when MMCP values were 5, 7, 9, and 1 (p < 0.05). With an increase in MMCP, the dose to OAR slightly increased, but the difference was not statistically significant (p > 0.05). With an increase in MMCP, the average number of MUs per segment significantly increased (p < 0.01). The groups based on MMCP values of 1, 3, 5, 7, and 9 received quality score system of 1.188, 1.180, 1.171, 0.987, and 1.184, respectively, with the MMCP7 group achieving the lowest score, indicating that this plan had the highest overall quality. The MMCP value for dynamic IMRT planning in the Monaco TPS for patients with NPC should be set to 7 to achieve fewer segments, the best execution efficiency without significantly deteriorating the target and OAR dose.

Dynamic and static nasolabial muscle anatomy of unilateral cleft lip adult patients based on magnetic resonance imaging data.

This study aims to obtain a three-dimensional reconstruction model based on magnetic resonance imaging (MRI) data of patients with different degrees of unilateral cleft lip and analyze the anatomy and changes in multiple groups of nasolabial muscles under dynamic and static conditions. One normal person and four adult patients with unilateral cleft lip were included, and MRI was performed under static (upper and lower lips closed naturally) and dynamic (pout and grin) conditions. 3D Slicer software was used to reconstruct the model and draw the anatomic morphology of nasolabial muscles. The distance between the junction (where the muscle merges into the orbicularis oris) of the levator muscle, zygomaticminor muscle, and zygomatic major muscle to the median sagittal plane, the starting point to the junction point, the dynamic and static junction points, and the angle between the connection of dynamic and static junctions and the horizontal plane were measured under three kinds of movements, and the ratio was calculated. In all patients, under dynamic and static conditions, the distance from the muscle junction to the median sagittal plane, their ratios of the cleft side to the non-cleft side were all greater than 1. While the ratio of the distance from the starting point of the muscle to the junction point is less than 1. At static conditions, the two ratios of the same muscle increased gradiently with the severity of the cleft, and the ratio of the zygomatic minor muscle was prominent in the same patient. The ratio of the cleft side to the non-cleft side was greater than 1, and the value for comparison was the angle of the line from the static to the dynamic junction and the horizontal plane. The symmetry of the insertion site of the orbicularis oris and the linear distance of both sides of the muscle are related to muscle and cleft types. The angle of muscle contraction on the cleft side is greater than that on the non-cleft side.

Method for Extracting Inland Unmanned Vessel Routes Based on AIS Data

This study introduces a novel approach to enhance inland navigation safety and optimize unmanned vessel routes using AIS data. The trajectory partition algorithm categorizes Yangtze River trajectory data to formulate round-trip routes. A turning point identification algorithm contributes to recognizing crucial turning points, followed by clustering using the HDBSCAN algorithm. Cluster centroids extracted from these clusters serve as essential waypoints for route planning. The Akima interpolation polynomial is applied judiciously to interpolate waypoints, culminating in meticulous route generation. Validation employs a dataset of 5,480,049 dynamic trajectory points from the Yangtze River, demonstrating the method's efficacy. Results indicate mean squared errors of 0.77% and 6.21%, symmetrical mean absolute percentage errors of 5.3% and 7.3%, and correlation coefficients of 99.62% and 97.14% with actual routes, respectively. This method adeptly extracts inland vessel round-trip routes, aligning seamlessly with stringent criteria for accuracy, effectiveness, and applicability.

Dynamic Global Maximum Power Point Tracking for Partially Shaded PV Arrays in Grid-Connected PV Systems

Dynamic Global Maximum Power Point Tracking for Partially Shaded PV Arrays in Grid-Connected PV Systems

A strain rate response and self-healing organohydrogel for use in impact-protective materials

Impact-protective materials are used in many fields owing to their excellent mechanical properties, impact resistance, and rapid responses to external stimuli. In this study, a new organohydrogel (PAIG) was designed and prepared through emulsion copolymerization by combining polymer chain segment as a skeleton and oil phase containing IHP as dynamic crosslinking points. The PAIG gel system provided a stable structure and overcame the cold flow property of impact-hardening polymers (IHP) with a tensile stress of 0.48 MPa. In addition, the introduced flexible IHP chains provided a large number of boron–oxygen dynamic bonds and hydrogen bonds, which improved both the impact resistance (the peak force reduction rate of 74 %), speed- response and the self-healing ability (with an efficiency of ca. 90 %). Impressively, PAIG can withstand cyclic twisting and bending even at −20 °C, showing its excellent freezing resistance. These properties make the PAIG promising for intelligent impact-protection applications.

Regional dynamic point cloud completion network

Point cloud completion network often encodes points into a global feature vector, then predicts the complete point cloud through the vector generation process. However, this method may not accurately capture complex shapes, as global feature vectors struggle to recover their detailed structure. In this paper, we present a novel shape completion network, namely RD-Net, that innovatively focuses on the interaction of information between points to provide both local and global information for generating fine-grained complete shape. Specifically, we propose a stored iteration-based method for point cloud sampling that quickly captures representative points within the point cloud. Subsequently, in order to better predict the shape and structure of the missing part, we design an iterative edge-convolution module. It uses a CNN-like hierarchy for feature extraction and learning context information. Moreover, we design a two-stage reconstruction process for latent vector decoding. We first employ a feature-points-based multi-scale generating decoder to estimate the missing point cloud hierarchically. This is followed by a self-attention mechanism that refines the generated shape and effectively generates structural details. By combining these innovations, RD-Net achieves a 2% reduction in CD error compared to the state-of-the-art method on the ShapeNet-part dataset.

The Development and Optimization of a New Wind Tunnel Design for Odour Sampling

The characterization of passive area sources, emitting odours due to wind-driven convection, poses significant challenges. The present experimental study aims to evaluate the performance, in terms of fluid dynamics and mass transfer, of a recently developed wind tunnel, with a more compact design and reduced weight, compared to the one proposed by the Italian regulations. The results show that the new design outperforms the Italian standard in several aspects. From a fluid dynamic point of view, the new wind tunnel exhibits a slightly more homogenous and uniform velocity distribution, and it does not reveal airflow preferential channels inside the central body. The pressure tests highlight that the presence of fillers in the new wind tunnel does not significantly alter the pressure inside the hood and therefore the gas–liquid equilibrium conditions; actually, the slight overpressure may help to prevent the infiltration of external air. Finally, mass transfer tests on the standard device show a vertical concentration gradient along the outlet duct, highlighting concentration values that differ up to a factor of two depending on the measurement point. The new design has almost completely solved this issue, thanks to the use of fillers that promote mixing of the outlet flow.

Explosive percolation in finite dimensions

Explosive percolation (EP) has received significant research attention due to its rich and anomalous phenomena near criticality. In our recent study [], we demonstrated that the correct critical behaviors of EP in infinite dimensions (complete graph) can be accurately extracted using the event-based method, with finite-size scaling behaviors still described by the standard finite-size scaling theory. We perform an extensive simulation of EPs on hypercubic lattices ranging from dimensions d=2 to 6, and find that the critical behaviors consistently obey the standard finite-size scaling theory. Consequently, we obtain a high-precision determination of the percolation thresholds and critical exponents, revealing that EPs governed by the product and sum rules belong to different universality classes. Remarkably, despite the mean of the dynamic pseudocritical point TL deviating from the infinite-lattice criticality by a distance determined by the d-dependent correlation-length exponent, TL follows a normal (Gaussian) distribution across all dimensions, with a standard deviation proportional to 1/V, where V denotes the system volume. A theoretical argument associated with the central-limit theorem is further proposed to understand the probability distribution of TL. These findings offer a comprehensive understanding of critical behaviors in EPs across various dimensions, revealing a different dimension-dependence compared to standard bond percolation. Published by the American Physical Society 2024

Effect of the Surface Peak-Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature.

This study explores the kinetic behavior of droplets impacting microtextured surfaces under a Leidenfrost temperature, employing high-speed photography and picosecond laser micromachining techniques. The investigation focuses on two types of microtextured surfaces with totally different surface peak-valley features: a negatively skewed surface with micropit arrays (Ssk < 0) and a positively skewed surface with micropillar arrays (Ssk > 0). The results indicate that both microtextured surfaces contribute to a higher Leidenfrost temperature compared with the original smooth surface, which is consistent with previous studies. However, it is worth noting that the Leidenfrost points of the micropit and micropillar surfaces showed opposite trends with the microtexture area occupancy. Specifically, the Leidenfrost temperature on micropit surfaces increases with greater micropit area occupancy, while it decreases on micropillar surfaces under similar conditions, which is mainly attributed to the differential impact of area occupancy on droplet heat transfer efficiency. When the microtexture area occupancy is 50%, it is worth noting that the micropit and micropillar surfaces have nearly same roughness (Sa), but the Leidenfrost temperature was notably higher on the micropit surface with negative skewness (Ssk < 0), which was related to differences in vapor flow dynamics. We further find that the Weber number (We) significantly influences the Leidenfrost point, with the droplet impact wall behavior going through the states of film bounce back, ejecting tiny droplets and bounce back, and ultimately droplet breakup as the We increases. The dynamic Leidenfrost point was found to be generally higher than the static point and increases with the We. Finally, we compare the cooling efficiency of these surfaces, and it is found that the micropit surfaces with a negative skewness exhibit superior heat dissipation performance under the same conditions, which proved that the negatively skewed surface may have great potential in high-density heat dissipation technology.

Leveraging occupancy map to accelerate video-based point cloud compression

Video-based Point Cloud Compression enables point cloud streaming over the internet by converting dynamic 3D point clouds to 2D geometry and attribute videos, which are then compressed using 2D video codecs like H.266/VVC. However, the complex encoding process of H.266/VVC, such as the quadtree with nested multi-type tree (QTMT) partition, greatly hinders the practical application of V-PCC. To address this issue, we propose a fast CU partition method dedicated to V-PCC to accelerate the coding process. Specifically, we classify coding units (CUs) of projected images into three categories based on the occupancy map of a point cloud: unoccupied, partially occupied, and fully occupied. Subsequently, we employ either statistic-based rules or machine-learning models to manage the partition of each category. For unoccupied CUs, we terminate the partition directly; for partially occupied CUs with explicit directions, we selectively skip certain partition candidates; for the remaining CUs (partially occupied CUs with complex directions and fully occupied CUs), we train an edge-driven LightGBM model to predict the partition probability of each partition candidate automatically. Only partitions with high probabilities are retained for further Rate–Distortion (R–D) decisions. Comprehensive experiments demonstrate the superior performance of our proposed method: under the V-PCC common test conditions, our method reduces encoding time by 52% and 44% in geometry and attribute, respectively, while incurring only 0.68% (0.66%) BD-Rate loss in D1 (D2) measurements and 0.79% (luma) BD-Rate loss in attribute, significantly surpassing state-of-the-art works.

Multi-level surfel map construction for accurate localization in intelligent vehicles

Abstract The role of mapping in achieving accurate localization in intelligent vehicles is crucial. Accurate maps enable intelligent vehicles to attain accuracy in localization and path planning, ultimately enhancing the safety and overall driving experience. However, the interference of dynamic objects poses a challenge to map updates and subsequent localization. To address this issue, this study proposes a novel algorithm for constructing multi-level semantic surfel maps, along with a corresponding hierarchical localization method. The proposed method divides the map into three levels: semantic level, surfel level, and trajectory level. During the map construction phase, we utilize semantic segmentation technology to accurately distinguish between static and dynamic environments, filter out dynamic point clouds, and focus solely on modeling static scenes. This effectively eliminates the interference of dynamic objects on map updates, ensuring the construction of a precise and reliable static environment model. In the subsequent hierarchical localization phase, a node-level localization method is introduced, which achieves coarse localization by matching perception information with the map.The results of experiments conducted in various scenarios indicate an average absolute trajectory error of 4cm between the transformation matrices of each node in the map. Our algorithm achieves a 92% success rate in node-level localization and an average pose-level localization error of 8.2cm, demonstrating the feasibility and robustness of the proposed algorithm.

YLS-SLAM: a real-time dynamic visual SLAM based on semantic segmentation

Purpose Traditional visual simultaneous localization and mapping (SLAM) systems are primarily based on the assumption that the environment is static, which makes them struggle with the interference caused by dynamic objects in complex industrial production environments. This paper aims to improve the stability of visual SLAM in complex dynamic environments through semantic segmentation and its optimization. Design/methodology/approach This paper proposes a real-time visual SLAM system for complex dynamic environments based on YOLOv5s semantic segmentation, named YLS-SLAM. The system combines semantic segmentation results and the boundary semantic enhancement algorithm. By recognizing and completing the semantic masks of dynamic objects from coarse to fine, it effectively eliminates the interference of dynamic feature points on the pose estimation and enhances the retention and extraction of prominent features in the background, thereby achieving stable operation of the system in complex dynamic environments. Findings Experiments on the Technische Universität München and Bonn data sets show that, under monocular and Red, Green, Blue - Depth modes, the localization accuracy of YLS-SLAM is significantly better than existing advanced dynamic SLAM methods, effectively improving the robustness of visual SLAM. Additionally, the authors also conducted tests using a monocular camera in a real industrial production environment, successfully validating its effectiveness and application potential in complex dynamic environment. Originality/value This paper combines semantic segmentation algorithms with boundary semantic enhancement algorithms to effectively achieve precise removal of dynamic objects and their edges, while ensuring the system's real-time performance, offering significant application value.