Point Trajectory Research Articles

Model-free control of a magnetically supported plate

Established model-based methods often use a combination of state feedback and observer to control complex systems. They rely on detailed mathematical models that are often hard to derive. Nonetheless, such methods may achieve a high level of accuracy, which justifies the cumbersome modelling. An alternative approach is model-free control, in a form introduced by Fliess and Join, where the system is approximated in a short time interval by a low-order differential equation with unknown parts, a so-called ultra-local model. This control method is a powerful tool, but the parametrisation and the concrete implementation may require time, effort, and experience. The present paper investigates the systematic tuning of a model-free controller for a magnetically supported plate that is modelled as an unstable multiple-input multiple-output system. Furthermore, the incorporation of model information into the model-free controller is investigated. These adaptations ultimately improve results by simplifying parameter tuning and interpretation of estimates. Several experiments are carried out on a test bed to show the capabilities of the proposed algorithms for set point stabilisation and trajectory tracking. The effects of the different parameters in the model-free controllers are addressed, and excellent robustness with respect to actuator faults is demonstrated. Filters for estimating derivatives and unknown quantities are designed using an open-source toolbox.

Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators

Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators

Three-Dimensional Radiation Analysis of the Hypersonic Jules Verne Reentry Spectra

The automatic transfer vehicle (ATV) Jules Verne, developed by the European Space Agency, played a crucial role in resupply missions to the International Space Station from 2008 to 2015. Following its resupply missions, Jules Verne was programmed to execute a planned destructive reentry into Earth’s atmosphere. To better understand the spectra generated by the Jules Verne, three-dimensional hypersonic flowfield and radiation analyses are performed at the 75 km trajectory point of the ATV. Results are compared to measurements obtained at 78 and 73 km by a miniature Echelle spectrograph operated from an airborne platform. Incorporating three-dimensional effects into the radiation analysis results in an overall reduction of the total radiation spectra and a decrease in atomic and molecular features, leading to a closer alignment between the observed and simulated values. Comparable effects are observed by making minor adjustments to the internal mode temperatures and modifying the model for the population of excited state number densities.

A Novel & Efficient LR LSTM AIS Route Data Prediction for Longer Range

The growth of technology has enabled different industries to generate an excessive amount of data- one such industry being the maritime sector. Sophisticated sensory systems installed on various vessels generate information at a very large scale which can further be used in optimizing operational efficiency, improving safety standards, and aiding in the decision-making process. Researchers have henceforth identified statistical learning methods and machine learning techniques as potent tools for excavating useful insights from this copious amount of data available. This research evaluates how these algorithms work by focusing exclusively on the analysis of sensory data collected from vessels within the maritime domain. A comparison study has been conducted between statistical learning methods (which includes regression analysis, and time series analysis) vis-a-vis machine learning approaches. The major objective of this study was to determine the most effective method for detecting anomalies while simplifying marine operations and optimizing vessel behavior. The scope of the conducted analysis is restricted to the prediction of the next trajectory points. Accurate prediction of vessel positions plays a crucial role in maritime operations, enabling efficient route planning, collision avoidance, and maritime traffic management. In this article, the authors propose a combination model that combines the benefits of Linear Regression (LR) and Long Short-Term Memory (LSTM) techniques to anticipate vessel positions based on Automatic Identification System (AIS) data. The proposed model takes advantage of the interpretability of LR and the temporal dependencies collected by LSTM to capture temporal dependencies, which improve prediction accuracy and reveal the underlying links between vessel features and future positions.

Collective search in ants: Movement determines footprints, and footprints influence movement.

Collectively searching animals might be expected to coordinate with their groupmates to cover ground more evenly or efficiently than uncoordinated groups. Communication can lead to coordination in many ways. Previous work in ants suggests that chemical 'footprints', left behind by individuals as they walk, might serve this function by modulating the movement patterns of following ants. Here, we test this hypothesis by considering the two predictions that, first, ants may turn away from sites with higher footprint concentrations (klinotaxis), or, second, that they may change their turning patterns depending on the presence of footprints (klinokinesis). We tracked 5 whole colonies of Temnothorax rugatulus ants in a large arena over 5h. We approximated the footprint concentration by summing ant visitations for each point in the arena and calculated the speed and local path straightness for each point of the ant trajectories. We counterintuitively find that ants walk slightly faster and straighter in areas with fewer footprints. This is partially explained by the effect that ants who start out from the nest walking straighter move on average further away from the nest, where there are naturally fewer footprints, leading to an apparent relationship between footprint density and straightness However, ants walk slightly faster and straighter off footprints even when controlling for this effect. We tested for klinotaxis by calculating the footprint concentrations perceived by the left and right antennae of ants and found no evidence for a turning-away (nor turning-towards) behavior. Instead, we found noticeable effects of environmental idiosyncrasies on the behavior of ants which are likely to overpower any reactions to pheromones. Our results indicate that search density around an ant colony is affected by several independent processes, including individual differences in movement pattern, local spatial heterogeneities, and ants' reactions to chemical footprints. The multitude of effects illustrates that non-communicative coordination, individual biases and interactions with the environment might have a greater impact on group search efficiency and exploratory movements than pheromone communication.

Abnormal-trajectory detection method based on sub-trajectory classification and outlier-factor acquisition

Abnormal-trajectory detection can be used to detect fraudulent behavior of taxi drivers transporting passengers. Aiming at the problem that existing methods do not fully consider abnormal fragments of trajectories, this paper proposes an abnormal-trajectory detection method based on sub-trajectory classification and outlier-factor acquisition, which effectively detects abnormal sub-trajectories and further detects abnormal trajectories. First, each trajectory is reconstructed using the turning angles and is divided into multiple sub-trajectories according to the turning angle threshold and trajectory point original acceleration. The sub-trajectories are then classified according to the extracted directional features. Finally, the multivariate distances between angular adjacent segments are calculated to obtain the outlier factor, and abnormal sub-trajectories are detected. The sum of the lengths of the abnormal sub-trajectories is used to calculate the outlier score and identify abnormal trajectories. Based on experimental results using real trajectory datasets, it has been found that the proposed method performs better at detecting abnormal trajectories than other similar methods.

An integrated method for compensating and correcting nonlinear error in five-axis machining utilizing cutter contacting point data

In current five-axis computer numerical control (CNC) machining, the use of minute linear path segments as an approximation for the ideal cutter contacting (CC) point trajectory is still prevalent. However, introducing rotation axes leads to a deviation of the actual CC point trajectory from the ideal, resulting in nonlinear errors. An integrated method is proposed in this paper for compensating and correcting both the contour error, associated with the approximation of the part surface by the ideal CC point trajectory and the nonlinear error of the CC point trajectory based on the information in the CC point data. By analyzing the spatial relationship between the tool posture and the CC point path during the five-axis linear interpolation process, two adjacent machining tool positions containing CC point data information are selected as the starting and ending points of the five-axis linear interpolation machining. The ideal tool center point and the actual CC point are calculated during the interpolation process, as well as the distance and the unit vector in the perpendicular direction between the actual CC point and the ideal CC point trajectory segment. In the comprehensive error compensation and correction phase, the obtained unit vectors are used as direction vectors for error compensation, and the tool center point during interpolation is first compensated and corrected. This ensures the actual CC point and the contour curve are on the same plane. The compensation direction for contour error is calculated using the start/end tool axis vectors and the ideal CC point trajectory vectors. The size of the contour error approximating the contour curve is calculated through the chord error. A second compensation and correction are applied to the tool center point for interpolation, ultimately achieving comprehensive compensation and correction of nonlinear errors. The data calculations were conducted in the MATLAB environment using actual machining data. After compensation and correction, the contour error was reduced by 76%, the nonlinear error of the CC point trajectory decreased to below 0.88 μm, and the comprehensive nonlinear error of the CC point trajectory was reduced from 19 to 1.5 μm, a reduction of 93%. This demonstrates significant practical value in enhancing the accuracy of five-axis CNC machining. Through actual machining verification, after using the method described in this paper, the average surface roughness decreased from 1.133 to 0.220 μm, and the maximum surface roughness decreased from 6.667 to 1.240 μm. This significantly demonstrates that the compensation and correction method proposed in this paper can significantly improve the surface quality of machined parts.

Constructing a non-degeneracy nD chaotic map model and counteracting dynamic degradation through adaptive impulsive perturbation

The characteristics of state point trajectory in phase space of a chaotic map, such as ergodicity, aperiodicity, broadband and noise-like, have been proved to be suitable for cryptography. However, some weaknesses, such as small key space, multiple bifurcations and dense windows that exist in some classic 1D and 2D chaotic maps, effect their practical application. To solve these problems, we constructed a non-degeneracy nD chaotic model, and instantiated it to obtain a 3D chaotic map (3D-NDCM), the dynamics analysis results demonstrated that the 3D-NDCM has a sufficiently large chaotic range, its state point trajectories has ergodicity in phase space. To counteract dynamic degradation and extend the aperiodic length of state time sequences, we further constructed a 6D-NDCM based on the model. Experimental analysis results demonstrated that, a state time sequence with longer aperiodicity can be obtained, through both dimension expansion and adaptive impulsive perturbation.

Control and physical verification of 6-DOF manipulator for power inspection robots based on expert PID algorithm

To enhance the performance of power inspection robots in intricate nuclear power stations, it is necessary to improve their response speed and accuracy. This paper uses the manipulator of the power inspection robot as the primary research object, and unlike previous control algorithm research, which only remained in the software simulation stage, we constructed a set of physical verification platforms based on CAN communication and physically verified the robotic arm’s control algorithm. First, the forward motion model is established based on the geometric structure of the manipulator and D-H parameter method, and the kinematic equation of the manipulator is solved by combining geometric method and algebraic method. Secondly, in order to conduct comparison tests, we designed PID controllers and expert PID controllers by utilising the expertise of experts. The results show that compared with the traditional PID algorithm, the expert PID algorithm has a faster response speed in the control process of the manipulator. It converges quickly in 0.75 s and has a smaller overshoot, with a maximum of only 6.9%. This confirms the expert PID algorithm’s good control effect on the robotic arm, allowing the six-degree-of-freedom robotic arm to travel more accurately and swiftly along the trajectory of the target point.

Patients' experiences of cancer immunotherapy with immune checkpoint inhibitors: A systematic review and thematic synthesis.

To obtain a deeper understanding of peoples' experiences of cancer treatments with immune checkpoint inhibitors (ICIs). ICIs are transforming survival outcomes for many with certain advanced cancers. Given the possibility of unique immune-related adverse events (irAEs), understanding treatment experiences is crucial to identify support needs and provide safe and effective person-centred care. A systematic review of qualitative research and thematic synthesis. To report this review, the Preferred Reporting Items for Systematic Analysis and Meta Analysis (PRISMA) checklist and Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) guidance have been used. MEDLINE, EMBASE, PsycINFO, CINAHL and Web of Science databases were searched in January 2022 for eligible studies published in English from database inception. Two reviewers independently screened records, identified papers for inclusion and appraised methodological quality using the Critical Appraisal Skills Programme checklist. Themes were developed using thematic synthesis. Eighteen papers were included and three analytical themes developed: immune checkpoint inhibitor treatment decision-making; the experience and impact of immune checkpoint inhibitor treatments; and appraising and responding to irAEs. The synthesis renders visible individuals' unmet information, psychological and practical support needs. It identifies shortcomings in immune checkpoint inhibitor treatment decision-making processes and highlights the need for healthcare professionals to recognise and sensitively handle individuals' treatment expectations. Individuals' understandings of and responses to irAEs are also illustrated, and attention drawn to patients' concerns about healthcare professionals' checkpoint inhibitor and irAEs knowledge. To sensitively manage treatment expectations and uncertainties, and optimise health outcomes, there are distinct points in treatment trajectories where care and support might require adapting and enhancing. This review addresses people's experiences of immune checkpoint inhibitor treatments. The core findings reveal unmet information, psychological and practical support needs. Insights derived from this review will enhance individuals' experiences and outcomes and healthcare professionals' practice. No patient or public involvement.

Browsing target extraction and spatiotemporal preference mining from the complex virtual trajectories

Public Map Service Platforms (PMSPs) aggregate and disseminate the earth observation data. Leveraging spatiotemporal preference patterns derived from browsing targets within complex virtual trajectories on PMSPs aids in constructing user-profiles and comprehending their intentions. However, complex virtual trajectories, characterized by numerous trajectory points and overlapping pyramidal spatial structures, introduce inefficiencies and inaccuracies during browsing target extraction. To mitigate this, we propose an Optimized Spatial Structure Segmentation (OSSS) method that divides complex virtual trajectories into sub-trajectories with simplified spatial structures, enhancing the efficiency of browsing target extraction. Spatiotemporal reconstruction of these sub-trajectories establishes sequences of browsing targets, revealing patterns of interest transitions. Moreover, recognizing the spatial uncertainty inherent in complex virtual trajectories, which results in imprecise matching between browsing targets and spatial features, we introduce a spatial co-occurrence semantic modeling approach. This involves constructing a POI semantic space and introducing a semantic similarity matching method to reduce spatial uncertainty and refine the accuracy of mining spatiotemporal preference patterns. We evaluate these methods using real-world data from Tianditu. Results demonstrate that the OSSS improves extraction efficiency by 3.45 times and accuracy by 18.84%. Additionally, the semantic similarity approach combined with spatial co-occurrence effectively mitigates spatial uncertainty. This research contributes to advancing the intelligence of PMSPs.

Research on the location of measurement points in explosion shock wave pressure testing

Shock wave is an important damage element in ammunition explosion and one of the important technical indicators for evaluating its damage power. Accurately measuring shock wave pressure is of great significance for guiding ammunition design and evaluating damage power. The testing environment in the explosion field is harsh, and the measurement of shock wave pressure is affected by various influencing factors, resulting in inaccurate measurement results and low reliability. The location of pressure measurement points directly affects the measurement results of surface reflection pressure and free field pressure. This study is based on the display explosion dynamics simulation software AUTODYN to conduct simulation analysis of the propagation law of explosion shock wave pressure, analyze the height variation of the three wave point trajectory during the ammunition explosion process, and clarify that the surface reflection pressure measurement points need to be located in the Mach reflection zone, and the free field pressure measurement points need to be located above the height of the three wave points. The research results provide theoretical support for accurate testing of explosion shock wave pressure and have significant engineering application value.

Modelling and estimating trajectory points from RTK-GNSS based on an integrated modelling approach

The sparse Gaussian process regression (GPR) has been used to model trajectory data from Real time kinematics-global navigation satellite system (RTK-GNSS). However, upon scrutinizing the model residuals; the sparse GPR model poorly fits the data and exhibits presence of correlated noise. This work attempts to address these issues by proposing an integrated modeling approach called GPR-LR-ARIMA where the sparse GPR was integrated with the linear regression with autoregressive integrated moving average errors (LR-ARIMA) to further enhance the description of the trajectory data. In this integrated approach, the predicted trajectory points from the GPR were further described by the LR-ARIMA. Simulation of the GPR-LR-ARIMA on three sets of trajectory data indicated better model fit, revealed in the normally distributed model residuals and symmetrically distributed scatter plots. Correlated noise was also successfully eliminated by the model. The GPR-LR-ARIMA outperformed both the GPR and LRARIMA by its ability to improve mean-absolute-error in 2-dimension positioning by up to 86%. The GPR-LR-ARIMA contributes to enhancement of positioning accuracy of dynamic GNSS measurements in localization and navigation system with good model fit.

A novel algorithm for ship characteristic points extraction based on density clustering

With the widespread application of ship Automatic Identification System (AIS) in maritime operations, a large number of ship trajectories become available. This study aims to improve the safety of ships navigating through densely trafficked areas and address the challenge of sufficient data exploration while fully describing the traffic conditions in these waters. To achieve this objective, traffic flow information is extracted from AIS data collected in Zhoushan waters. A combination of multi-algorithms is employed to extract the traffic flow frame, specifically, the Douglas-Peucker compression algorithm and trajectory intersection algorithm are utilised to identify the characteristic points of ship trajectories. Subsequently, a density clustering algorithm is applied to extract the three types of characteristic points: compressed trajectory points, intersection points, and ship position points, facilitating data mining efforts. The resulting initial traffic flow characteristic points are then subject to weighted fusion, followed by image superposition processing to create an overlapping map of the ship trajectories. This process culminates in the generation of a traffic flow frame for the region. The framework integrates various track characteristic points, offering insights into the distribution of essential routes in the vicinity waters, thereby providing a comprehensive depiction of ship traffic flow patterns. The proposed framework can be applied to the route planning and serve as a reference to the maritime authorities when selecting recommended shipping lanes.

Interpolation by polygon rolling motions for approximate sweep computation

We interpolate planar rigid body motions by piecewise rotational and translational motions. The trajectories of all points are then arc splines, i.e., curves composed of circular arcs or line segments. For objects with arc spline boundaries, the boundary of the volume swept by the object and the motion is then again an arc spline. We prove that the distance between the original motion and its approximation as between curves in a suitable kinematic image space converges quadratically to zero. This implies the same rate of convergence between point trajectories under the two motions.

Dynamic Multiple Object Segmentation with Spatio-Temporal Filtering.

This article primarily focuses on the localization and extraction of multiple moving objects in images taken from a moving camera platform, such as image sequences captured by drones. The positions of moving objects in the images are influenced by both the camera's motion and the movement of the objects themselves, while the background position in the images is related to the camera's motion. The main objective of this article was to extract all moving objects from the background in an image. We first constructed a motion feature space containing motion distance and direction, to map the trajectories of feature points. Subsequently, we employed a clustering algorithm based on trajectory distinctiveness to differentiate between moving objects and the background, as well as feature points corresponding to different moving objects. The pixels between the feature points were then designated as source points. Within local regions, complete moving objects were segmented by identifying these pixels. We validated the algorithm on some sequences in the Video Verification of Identity (VIVID) program database and compared it with relevant algorithms. The experimental results demonstrated that, in the test sequences when the feature point trajectories exceed 10 frames, there was a significant difference in the feature space between the feature points on the moving objects and those on the background. Correctly classified frames with feature points accounted for 67% of the total frames.The positions of the moving objects in the images were accurately localized, with an average IOU value of 0.76 and an average contour accuracy of 0.57. This indicated that our algorithm effectively localized and segmented the moving objects in images captured by moving cameras.

KGTS: Contrastive Trajectory Similarity Learning over Prompt Knowledge Graph Embedding

Trajectory similarity computation serves as a fundamental functionality of various spatial information applications. Although existing deep learning similarity computation methods offer better efficiency and accuracy than non-learning solutions, they are still immature in trajectory embedding and suffer from poor generality and heavy preprocessing for training. Targeting these limitations, we propose a novel framework named KGTS based on knowledge graph grid embedding, prompt trajectory embedding, and unsupervised contrastive learning for improved trajectory similarity computation. Specifically, we first embed map grids with a GRot embedding method to vigorously grasp the neighbouring relations of grids. Then, a prompt trajectory embedding network incorporates the resulting grid embedding and extracts trajectory structure and point order information. It is trained by unsupervised contrastive learning, which not only alleviates the heavy preprocessing burden but also provides exceptional generality with creatively designed strategies for positive sample generation. The prompt trajectory embedding adopts a customized prompt paradigm to mitigate the gap between the grid embedding and the trajectory embedding. Extensive experiments on two real-world trajectory datasets demonstrate the superior performance of KGTS over state-of-the-art methods.

Deciphering the molecular landscape: integrating single-cell transcriptomics to unravel myofibroblast dynamics and therapeutic targets in clear cell renal cell carcinomas.

Clear cell renal cell carcinomas (ccRCCs) epitomize the most formidable clinical subtype among renal neoplasms. While the impact of tumor-associated fibroblasts on ccRCC progression is duly acknowledged, a paucity of literature exists elucidating the intricate mechanisms and signaling pathways operative at the individual cellular level. Employing single-cell transcriptomic analysis, we meticulously curated UMAP profiles spanning substantial ccRCC populations, delving into the composition and intrinsic signaling pathways of these cohorts. Additionally, Myofibroblasts were fastidiously categorized into discrete subpopulations, with a thorough elucidation of the temporal trajectory relationships between these subpopulations. We further probed the cellular interaction pathways connecting pivotal subpopulations with tumors. Our endeavor also encompassed the identification of prognostic genes associated with these subpopulations through Bulk RNA-seq, subsequently validated through empirical experimentation. A notable escalation in the nFeature and nCount of Myofibroblasts and EPCs within ccRCCs was observed, notably enriched in oxidation-related pathways. This phenomenon is postulated to be closely associated with the heightened metabolic activities of Myofibroblasts and EPCs. The Myofibroblasts subpopulation, denoted as C3 HMGA1+ Myofibroblasts, emerges as a pivotal subset, displaying low differentiation and positioning itself at the terminal point of the temporal trajectory. Intriguingly, these cells exhibit a high degree of interaction with tumor cells through the MPZ signaling pathway network, suggesting that Myofibroblasts may facilitate tumor progression via this pathway. Prognostic genes associated with C3 were identified, among which TUBB3 is implicated in potential resistance to tumor recurrence. Finally, experimental validation revealed that the knockout of the key gene within the MPZ pathway, MPZL1, can inhibit tumor activity, proliferation, invasion, and migration capabilities. This investigation delves into the intricate mechanisms and interaction pathways between Myofibroblasts and ccRCCs at the single-cell level. We propose that targeting MPZL1 and the oxidative phosphorylation pathway could serve as potential key targets for treating the progression and recurrence of ccRCC. This discovery paves the way for new directions in the treatment and prognosis diagnosis of ccRCC in the future.

ConvTEBiLSTM: A Neural Network Fusing Local and Global Trajectory Features for Field-Road Mode Classification

Field-road mode classification (FRMC) that identifies “in-field” and “on-road” categories for Global Navigation Satellite System (GNSS) trajectory points of agricultural machinery containing geographic information is essential for effective crop improvement. Most previous studies utilize local trajectory features (i.e., the relationships between a point and its neighboring points), but they ignore global trajectory features (i.e., the relationships between the point and all points of the trajectory), leading to difficulty in improving the overall classification performance. The global trajectory features are useful for FRMC because they contain rich trajectory information (e.g., mode switching and motion tendency). Therefore, a ConvTEBiLSTM network-based method is proposed to improve the overall performance. Firstly, nine statistical features (e.g., speed and direction) are extracted from the original data and fed into the ConvTEBiLSTM network. Then, the ConvTEBiLSTM network combining the Bidirectional Long Short-Term Memory network, 1D Convolution network, and Transformer-Encoder network is used to extract and fuse local and global trajectory features. Finally, a linear classifier is applied to identify the “field” and “road” categories of GNSS points based on the fused features. Experimental results show that compared with the baselines, our method achieves the best accuracy and F1-score of 97.38% and 92.74% on our Harvester dataset, respectively.

Empirical investigation of passive blood drop trajectory and first point of contact on inclined surfaces

The first point of contact between a spherical blood drop and a surface is related to the angle between the trajectory of the blood drop and the surface being struck. This angle is often referred to as the impact angle which can be estimated by knowing the width and length of the resultant elliptical bloodstain. Most software programs dedicated to area of origin analysis indicate the location of the backtracked bloodstain trajectory to be at the geometric centre or at the tip of the bloodstain ellipse. However, it is unknown how the first point of contact and the blood drop trajectory (here defined as the locus of the centre of mass of the drop as it travels) are related empirically. Thus, this study aims to look at how the initial point of contact and the trajectory at the impact of a blood drop relates to the formed bloodstain ellipse. Two volumes of blood (0.013 ml and 0.071 ml) were dropped from a height of 10 cm and 40 cm onto an inclined surface at 0°, 15°, 30°, 45°, 60°, and 75°. The transition from a spherical blood drop to an elliptically shaped bloodstain was recorded using a high-speed camera for all tests. A total of 72 ellipses were analyzed to determine the location of the first point of contact and trajectory point of the blood drop and how they relate to the formed elliptical bloodstain. A relationship was found between the first point of contact and the bloodstain trajectory which was dependent on the impact angle. However, there were clear deviations from theoretical assumptions due to blood drop oscillations, the effects of gravity, and the natural fluid characteristics of blood. The results of this study may assist bloodstain pattern analysts and software developers by more accurately applying the location of the blood drop trajectory based on empirical data.