Position Data Research Articles

Study on Measurement Method of Three-dimensional Position of Unlabeled Microspheres under Bright Background

Abstract: Using computer vision technology to obtain the position and trajectory data of particle probe microspheres from microscope images has significance and value in the molecular field. However, most of the existing microsphere measurement methods are based on transmission, which can only be measured under transparent samples and substrates and are not suitable for the application scenario of living cell measurement. In this paper, a method based on reflectivity imaging is proposed to measure the three-dimensional position of the dark microspheres in the bright field. Based on the outermost ring radius method, the relationship between the inner ring radius of the microsphere spot and the out-of-focus distance was explored to measure the coordinates in the Z direction. Cardiomyocytes were combined with 10um size silica microspheres. Experiments show that in a bright field with a high perturbation environment, it can achieve high precision measurement of dark microspheres and achieve three-dimensional position measurement with an accuracy of 50nm in XY direction and 100nm in Z direction.

Vision-Based Detection of Unsafe Worker Guardrail Climbing Based on Posture and Instance Segmentation Data Fusion

Vision-Based Detection of Unsafe Worker Guardrail Climbing Based on Posture and Instance Segmentation Data Fusion

Prediction of the Relative Resource Abundance of the Argentine Shortfin Squid Illex argentinus in the High Sea in the Southwest Atlantic Based on a Deep Learning Model

To analyze the impact of the marine environment on the relative abundance of Illex argentinus (high and low categories) in the southwest Atlantic, this study collected logbook data from Chinese pelagic trawlers from December 2014 to June 2024, including vessel position data and oceanographic variables such as sea surface temperature, 50 m and 100 m water temperature, sea surface salinity, sea surface height, chlorophyll-a concentration, and mixed layer depth. Vessel positions were used to enhance the logbook data quality, allowing an analysis of the annual trends in the resource center of this squid at a spatial resolution of 0.1° × 0.1° and a temporal resolution of ten days. The findings showed that the resource center is primarily located around 42° S in the north and between 45° S and 47° S in the south, with a trend of northward movement during the study period. Additionally, we constructed two ensemble learning models based on decision trees—AdaBoost and PSO-RF—aiming to identify the most critical environmental factors that affect its resource abundance; we found that the optimal model was the PSO-RF model with max_depth of 5 and n_estimators of 46. The importance analysis revealed that sea surface temperature, mixed layer depth, sea surface height, sea surface salinity, and 50 m water temperature are critical environmental factors affecting this species’ resources. Given that deep learning models generally have shorter running times and higher accuracy than other models, we developed a CNN-Attention model based on the five most important input factors. This model achieved an accuracy of 73.6% in forecasting this squid for 2024, predicting that the population would first appear near the Argentine exclusive economic zone around mid-December 2023 and gradually move east and south thereafter. The predictions of the model, validated through log data, maintained over 70% accuracy during most periods at a time scale of ten days. The successful construction of the resource abundance forecasting model and its accuracy improvements can help enterprises save fuel and time costs associated with blind searches for target species. Moreover, this research contributes to improving resource utilization efficiency and reducing fishing duration, thereby aiding in lowering carbon emissions from pelagic trawling activities, offering valuable insights for the sustainable development of this species’ resources.

Measuring daily tourism mobility spillover at the intra-metropolis level with mobile positioning data

ABSTRACT Although tourism mobility spillover continues to be a key indicator for tourism management, more innovative research must be conducted at the micro level and high sampling frequency. Against the backdrop of an increasing number of global cities, in this paper, we evaluate the daily tourism mobility spillover inside a worldwide city of China: Shanghai. Based on the Granger causal network model and an original mobile positioning dataset, we analyse the causal relationship between local tourism flows and the spillover effects of tourism mobility within Shanghai. By categorising tourists into ‘local tourists from Shanghai’ and ‘tourists from out of Shanghai’, we reveal a significant causal relationship between Shanghai districts and flows generated by ‘tourists from out of Shanghai’. The analysis of the causal network structure also reveals key districts and points of interest that significantly contribute to congestion in tourism mobility and Shanghai's dynamics. This econometric approach offers policymakers a valuable tool to monitor mobility drivers and optimise flows within the city.

A position-based method for detecting orbital angular momentum modes

Abstract Due to the inherent limitations of current Orbital Angular Momentum (OAM) mode detection methods and constraints posed by the physical size of receiving antennas, accurately identifying higher-order OAM modes represents a significant challenge. Therefore, there is a pressing necessity to overcome these hurdles. This paper introduces a new approach to OAM mode detection that exploits positional information. Our methodology ascertains the OAM mode by examining the phase and position data of the electric field at any two randomly chosen points within the energy's spatial distribution radiated by vortex electromagnetic waves. This innovation promises not only to precisely detect higher-order OAM modes but also grants greater versatility in the placement of receiving antennas. Moreover, the study delves into the correlation between the electric field phase of vortex electromagnetic waves, the azimuth angle, and the receiving distance. The validity of our approach is confirmed through both simulation outcomes and experimental data derived from physical tests.

Data fusion method for aircraft surveillance in flight zone based on Trans-Attention

Aiming at the problem of low monitoring accuracy and position jump of single monitoring source of aircraft in the flight zone, a method of aircraft monitoring data fusion based on Transformer and attention mechanism is proposed. Firstly, the encoder structure of Transformer is used to extract features of each monitoring source data respectively, and then weight values are assigned to different monitoring sources through the attention mechanism. Finally, regression calculation is performed through the fully connected network to obtain the final fusion result. The monitoring data of the surface surveillance radar and the broadcast automatic dependent surveillance system are selected as the fusion source, and the multi-point positioning data is used as the true label. The experimental results show that this method effectively reduces the monitoring error of a single monitoring source, and the fusion effect is better than the long short-term memory network, recurrent neural network and extended Kalman filter fusion method based on the attention mechanism, and the mean absolute error is improved by 2.20%、14.32%and respectively 33.94%.

STCA: High-Altitude Tracking via Single-Drone Tracking and Cross-Drone Association

In this paper, we introduce a high-altitude multi-drone multi-target (HAMDMT) tracking method called STCA, which aims to collaboratively track similar targets that are easily confused. We approach this challenge by categorizing the HAMDMT tracking into two principal tasks: Single-Drone Tracking and Cross-Drone Association. Single-Drone Tracking employs positional and appearance data vectors to overcome the challenges arising from similar target appearances within the field of view of a single drone. The Cross-Drone Association employs image-matching technology (LightGlue) to ascertain the topological relationships between images captured by disparate drones, thereby accurately determining the associations between targets across multiple drones. In Cross-Drone Association, we enhanced LightGlue into a more efficacious method, designated T-LightGlue, for cross-drone target tracking. This approach markedly accelerates the tracking process while reducing indicator dropout. To narrow down the range of targets involved in the cross-drone association, we develop a Common View Area Model based on the four vertices of the image. Considering to mitigate the occlusion encountered by high-altitude drones, we design a Local-Matching Model that assigns the same ID to the mutually nearest pair of targets from different drones after mapping the centroids of the targets across drones. The MDMT dataset is the only one captured by a high-altitude drone and contains a substantial number of similar vehicles. In the MDMT dataset, the STCA achieves the highest MOTA in Single-Drone Tracking, with the IDF1 system achieving the second-highest performance and the MDA system achieving the highest performance in Cross-Drone Association.

Methods for Evaluating Tibial Accelerations and Spatiotemporal Gait Parameters during Unsupervised Outdoor Movement.

The purpose of this paper is to introduce a method of measuring spatiotemporal gait patterns, tibial accelerations, and heart rate that are matched with high resolution geographical terrain features using publicly available data. These methods were demonstrated using data from 218 Marines, who completed loaded outdoor ruck hikes between 5-20 km over varying terrain. Each participant was instrumented with two inertial measurement units (IMUs) and a GPS watch. Custom code synchronized accelerometer and positional data without a priori sensor synchronization, calibrated orientation of the IMUs in the tibial reference frame, detected and separated only periods of walking or running, and computed acceleration and spatiotemporal outcomes. GPS positional data were georeferenced with geographic information system (GIS) maps to extract terrain features such as slope, altitude, and surface conditions. This paper reveals the ease at which similar data can be gathered among relatively large groups of people with minimal setup and automated data processing. The methods described here can be adapted to other populations and similar ground-based activities such as skiing or trail running.

Structures and properties of the late pleistocene paleosols of the loess-soil section of Obi-Mazar (Tajikistan)

The loess-soil series of Central Asia cover the history of subaerial sedimentation of the last 2–2.5 mln years. Significant thicknesses, a large number of paleosols, and an impressive chronology place the loess-soil series of the Afghan-Tajik depression on a par with the famous sections of the Loess Plateau of China. The study of the most significant sections located within the Khovaling Loess Plateau makes it possible to develop a regional chronostratigraphic chart, to study the structure and conditions of formation of the main stratigraphic benchmarks–buried paleosols. The present study is devoted to the clarification of the structural features of loess and paleosols of the Late Pleistocene in one of the sections of the Khovaling–Obi-Mazar Plateau. Based on the results of stratigraphic dissection, description of the macro- and micromorphological structure, magnetic susceptibility analysis, study of the chemical and granulometric composition, a comprehensive description of the structure and properties was carried out and the most probable conditions for the formation of paleosols and loess were established. In the Late Pleistocene part of the section, two loess layers and a pedocomplex PC1 with three welded paleosols, consisting of a total of 7 horizons, are distinguished. The deposits are characterized by high silt content, carbon content, and the presence of signs of pedogenesis and biological activity in all layers of the studied section. Available data indicate the presence of a poorly developed MIS 3 paleosol in the upper loess layer. The developed pedocomplex PC1 of Obi-Mazar, according to its stratigraphic position, structural features, and magnetic susceptibility data, belongs to the MIS 5. According to preliminary data, its formation occurred in semi-humid and humid conditions under forest vegetation, which contributed to the biogenic segregation of Fe-Mn compounds, as well as intra- and interhorizon redistribution of carbonates due to diagenetic processes. Towards the end of the Last Interglacial, gradual aridization occurred, due to which the upper paleosol to be the least developed.

The application of improved DTW algorithm in sports posture recognition

Sports posture recognition plays a crucial role in modern sports science and training. Posture recognition and analysis plays a positive role in improving sports quality and ensuring sports safety. However, existing recognition technologies still have poor recognition and accuracy in large amounts of posture data. Therefore, to further improve the performance of the existing posture recognition techniques, this study assumes that postures during movement can be effectively represented through the time series of skeletal key points, and the local similarity of these postures can be captured through the Dynamic Time Warping (DTW) algorithm. Based on this assumption, the existing DTW algorithm is improved by introducing the K-Nearest Neighbor (KNN) algorithm and combining it with Principal Component Analysis (PCA) for feature dimensionality reduction. A novel algorithmic model for postures recognition is proposed. The experimental results showed that the improved algorithm performed well in postures recognition rate and accuracy. Especially, when the k value was 5, the recognition rate reached up to 89%, and the accuracy reached 87%. Compared with the existing algorithm, the improved KNN-DTW algorithm has significant improvement in accuracy and computational efficiency. In summary, the new algorithm shows significant advantages in terms of accuracy and stability, providing a powerful tool for the analysis of athletic postures in the field of sports. Meanwhile, this research result has important application prospects in fields such as sports training, sports medicine, and virtual reality.

Submaximal force–velocity relationships during mountain ultramarathon: Data from the field

ABSTRACT This study presents a novel method for evaluating the submaximal velocity-force (V(F)) relationship in mountain ultramarathon races using crowdsourced data from Strava.com. The dataset includes positional data from 408 participants of the 171-km UTMB® 2023 race (9,850-m D+). The race was divided into 100-m segments. The mean net propulsive force and velocity were computed for each segment to describe the submaximal V(F) relationship as a rational function of three parameters. F1: propulsive force at 1 m · s−1; V0: theoretical maximum velocity on flat terrain; C: curvature parameter (the lower C, the more linear the V(F) relationship). The V(F) profile parameters were found to be F1 = 1.80 ± 0.33 N · kg−1, V0 = 2.36 ± 0.42 m · s−1, and C = 0.66 ± 1.81, with good independence between the parameters within a group of homogeneous performance. The best athletes had the highest F1, V0, and C values. V(F) parameters were affected by fatigue during the race, with decreases of 20.9%, 32.0%, and 59.8% between the first and second parts of the race respectively. These findings suggest that the V(F) relationship is an interesting original approach for studying performance and fatigability during mountain ultra-endurance races.

Mapping pathogenic fungi in decayed Norway spruce stands: Insights from harvester positional data

Abstract Root and butt rot caused by pathogenic fungi in the genera Heterobasidion and Armillaria is a pressing issue in managed Norway spruce forests. The disease results in financial losses for the forest owners and reduces the volume of wood that can be used in long‐lived products. Pathogenic wood decay fungi spread either with the aid of airborne spores or via mycelial growth among neighbouring trees, the latter leading to clustering (tendency of decayed trees to be in close proximity relative to their neighbouring trees) of decay‐affected trees in forests. Understanding the spatial patterns of the decay‐affected trees at the forest stand level is vital for designing management strategies to address this problem. We examined decay clustering in 273 clear‐cut Norway spruce stands in Norway using harvester‐recorded data on spatial occurrence of decayed and healthy Norway spruce trees. We tested clustering using three global‐cluster tests that account for population density and distribution, evaluating clustering without identifying specific cluster locations. The proportions of clustered and non‐clustered stands differed depending on the statistical test used for clustering assessment, resulting in overall agreement of 32.8% for clustered and 36.9% for non‐clustered. Clustered stands exhibited a median cluster distance (maximum distance between the decay‐affected trees within a cluster) of 12 m (Inter‐Quantile Range, IQR, 6–20 m) and a median of 6 (IQR 3–16) nearest neighbour trees (number of decayed trees forming a cluster), estimates comparable with prior studies focused on assessment of trees infected by mycelial spread of the same fungal individual. The decay incidence in the clustered stands was 16.24%, while the non‐clustered stands had a butt‐rot incidence of 20.97%. In clustered stands the average number of trees per hectare was higher (693) than in non‐clustered stands (553). Synthesis and applications: Our study demonstrates that Norway spruce stands display a diverse range of spatial patterns of butt rotted trees. We found that higher densities of Norway spruce trees probably facilitate the vegetative spread of pathogenic wood decay fungi, leading to clustering of decay‐affected trees. To disrupt the spread of decay fungi between tree generations, precision planting of trees other than Norway spruce around infested stumps of prior generation trees has been recommended by earlier studies. We discussed the potential of using harvester‐derived geoposition data for butt‐rotted trees upon planning and execution of forest regeneration.

Exploring the use of wearable sensors for assessing risk factors during orthopedic surgeries: A protocol for data collection

During orthopedic surgeries, surgeons are generally exposed to prolonged periods of standing, awkward and sustained body postures, and forceful movements, which can increase the likelihood of developing work-related musculoskeletal disorders (WRMSD). Therefore, this study proposes a protocol to measure parameters related to physical risk factors contributing to lower limb WRMSD, during orthopedic surgery procedures. The protocol development was preceded by an initial phase of understanding and specifying the context of use, followed by pre-tests in laboratory environment. It integrates a motion capture system, using inertial measurement units (IMU) to collect posture data from hip, knee, and ankle, and electromyography system (EMG) to measure and record data from muscle activity of biceps femoris, rectus femoris, and gastrocnemius lateralis. Pre-tests provided insights for protocol optimization, estimating a 3-hour data collection session per surgery due to sensor battery limitations, streamlining the process by placing EMG sensors before IMU and refining thigh sensor placement strategies. The protocol presents an opportunity for a real-time and quantitative approach to monitor surgeon's exposure to risk factors contributing to lower limb WRMSD while performing surgical procedures. Two months after pre-tests, the protocol implementation began in a real work context. The study's final outcomes fall outside the paper's scope.

Mapping Visit Behaviors in a Virtual Art Gallery to Visitor Engagement Profiles Using Latent Class Analysis

Virtual art galleries represent a rapidly growing and potentially unique context for art engagement, but little is known about the psychological nature of these experiences. To better characterize virtual art gallery visits, we quantified a set of in-depth behaviors related to art viewing, navigation, and intensive art engagement in a virtual gallery. Data included coordinate position and gaze proxy data, recorded from 264 unconstrained participant visits to a virtual gallery that contained 24 diverse artworks curated throughout 3 rooms. Nine classified visit behaviors—related to the overall visit, navigation, art viewing, and deep engagement—were used in a latent class analysis as indicators for estimating possible visitor engagement classes. The analysis resulted in a 4-class model which classified visitors as disengaged, typical, engaged, and revisitors. Finally, we examined how each class differed in terms of their art knowledge, personality, and other individual differences. Significant differences were found for several variables, including aesthetic fluency, openness to experience, extraversion, age, and nausea.

Accuracy verification of the 2D spectral AIS method by the hull monitoring data

Accuracy validation of the 2D spectral AIS method (AIS method using two-dimensional wave spectrum; 2D-AIS method) using hull monitoring data of a large ore carrier is discussed. The AIS method predicts the response of actual ships using AIS-based ship position data, hindcast wave data, and response characteristics data. The conventional AIS method uses representative parameters(H, T, θ) from wave spectrum modeling. The modeling involves many uncertainties. By using the 2D wave spectrum as it is, the effect of uncertainties associated with wave spectrum modeling is investigated. Compared to the analysis of the monitoring data conducted in this study, it was found that the 2D-AIS method improves the accuracy to about 15 %, while the conventional AIS method shows an error of up to about 35 % compared to the actual measurements in the long-term maximum expected values. This enhancement is attributed to the inability of the modeled wave spectrum to accurately represent the two-peak wave spectrum observed in extreme sea states, leading to a deficiency in reproducing wave frequency components corresponding to peak frequencies of the response. The findings underscore the effectiveness of the 2D-AIS method in refining long-term hull stress estimations, thus offering valuable insights for enhancing the safety and performance of hull structures.

PPP time transfer using an adaptive clock constraint model

ABSTRACT In the processing of Precise Point Positioning (PPP) data, the receiver clock is approached with nearly infinite uncertainty, rendering it difficult to fulfill the requirements of high-precision time frequency applications. Therefore, a receiver clock model is essential. In this study, we first analyze the “over-constraint” problem in the existing clock model and subsequently propose a new clock model, called the Adaptive Clock Constraint (ACC) model, which relies on a sliding window to update covariance and frequency characteristics parameters in real-time. To verify the robustness of the ACC model, three experiments were conducted, and the results show that the ACC model not only is suitable for different types of atomic clock stations but also has superior frequency stability and time transfer precision in contrast to the BIPM PPP, the IGS products and White Noise (WN) model results. Using the optical fiber results as a time reference, the STD of the time difference between the ACC model and optical fiber results is 0.13 ns and the frequency stability is 1.28 × 10−16 on average for one week, representing improvements of more than 10% and 15% compared with the BIPM PPP results.

Application of FFBP neural network to the prediction of swirling flow

Since ancient times, man has been seeking to improve in the field of transportation and energy consumption, as the combination of optimal energy consumption and not polluting the environment in exchange for obtaining a better return made him constantly searching for optimal methods in the field of anxiety and industry based on combustion. The swirl or vortex flow is one of the most important discoveries of the last century seeking to develop combustion, as research is still focused on it in many aspects, whether in terms of the shape of the rotational areas it forms. This study aims to determine the extent to which artificial intelligence can predict the characteristics of the Swirl flow. The model adopted experimental data of the Swirl flow, both descriptive and positional data as inputs and horizontal, vertical, and kinetic energy as outputs for several positions within the combustion chamber. The results indicate that the model can capture the spatial characteristics of the vortex flow field and the combined Learning of the relationship between the input and output parameters. The results of the velocity density distribution and the position of the vortex center of the vortex flow field agree well with the experimental results. The generated prediction model has good prediction accuracy based on previously observed datasets and can reconstruct the vortex flow field. In addition, it can perform inductive prediction on a previously unseen dataset and has some generalization ability. Finally, this study suggests that many potential architectures have applications based on the induction prediction model created here.

A novel approach to improve population mapping considering facility-based service capacity and land livability

Social sensing data, including points-of-interest (POI) and mobile position, are important data sources for population mapping. However, existing studies using POI data disregard the heterogeneity in facility size among the same type of POI and urban–rural differences. Moreover, mobile position data face biased issues. This study presents a hybrid model that considers facility-based service capacity (FSC) and land livability to map fine-scale population distributions. Based on extracting the FSC index by integrating POI, mobile phone positioning (MPP), and road network data, the district-level census population was disaggregated into 100-m grids using a random forest model. Subsequently, a regression equation was developed from the land use data to correct the estimated residuals. The results showed that the hybrid model exhibited considerably smaller errors than those of the POI density-based method and direct mapping of MPP (RMSE = 5,393.31, 7,348.91, and 7,824.76, respectively), effectively reducing population misallocation in extreme-density regions. Compared to WorldPop and LandScan datasets and a comparative model, our method reduced the RMSE by 30–60%, proving the effectiveness of integrating various social sensing and remote sensing data for improving population mapping. This study improves on an existing method as a thought-provoking step toward advancement in fine-scale population mapping research.

Method for robot kinematic parameters identification based on position and orientation data obtained with laser tracker.

How to quickly and accurately identify the kinematic parameter errors is an important prerequisite for robot accuracy compensation. The laser tracker is used to measure and identify the kinematic parameters of the robot. The influence of laser tracker layout in robot pose measurement is analyzed, and the evaluation function of laser tracker layout is constructed to determine the optimal layout position. Then, the mapping relationship between the position and orientation deviation of the robot end and the deviation of each kinematic parameter is established, and the kinematic parameter identification model integrating the position and orientation information is constructed. The identification model is compared to the identification model based on position information to clarify the intrinsic reason of high accuracy. Next, aiming at the problem that the genetic algorithm is easy to prematurely converge to the local optimal solution, a hybrid genetic algorithm is constructed by introducing the simplex method to determine the optimal calibration pose set of the robot. On this basis, the results of robot kinematics parameter identification based on position measurement data, pose measurement data, and optimal calibration pose set are compared and analyzed through experiments, which verifies the effectiveness of the robot kinematics parameter identification model based on pose measurement data and optimization strategy.

A star selection strategy based on MDS and fast convex hull algorithm in COSPAS-SARSAT system

Abstract The space segment of the current COSPAS-SARSAT system is mainly supported by the Galileo system, and the positioning technology used is generally three-star time-difference positioning. Most of the antennas equipped in the Mission Control Centre (MCC) are not omnidirectional. In the case of Source of Search and Rescue (SAR) in a heavily shaded environment, it is necessary to use the different orbit time-sharing single satellite geolocations using TDOA, and the time-sharing will generate more satellite position data. In order to achieve the higher positioning accuracy of the solution, it is necessary to select the appropriate time and optimal geometrical distribution from the satellite positions. In this paper, we propose a satellite selection strategy based on Multiple Dimensional scaling (MDS) data degradation and a fast convex hull algorithm. The three-dimensional satellite data are downscaled to two-dimensional while maintaining their relative positional relationships, and the two-dimensional convex hull algorithm is used for Geometric Dilution of Precision (GDOP) minimum search star selection. By simulating the satellite position data in different orbits and time-sharing to complete the localization solution, the results show that compared with the traditional satellite selection method, the satellite selection strategy proposed in this paper is able to achieve better localization accuracy in a shorter period of time.