• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.94-102
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• 2023

Anomaly detection in human movements can improve safety in indoor workplaces. In this paper, we design a framework for detecting anomalous trajectories of humans in indoor spaces based on a variational autoencoder (VAE) with Bi-LSTM layers. First, the VAE is trained to capture the latent representation of normal trajectories. Then the abnormality of a new trajectory is checked using the trained VAE. In this step, the anomaly score of the trajectory is determined using the trajectory reconstruction error through the VAE. If the anomaly score exceeds a threshold, the trajectory is detected as an anomaly. To select the anomaly threshold, a new metric called D-score is proposed, which measures the difference between recall and precision. The anomaly threshold is selected according to the minimum value of the D-score on the validation set. The MIT Badge dataset, which is a real trajectory dataset of workers in indoor space, is used to evaluate the proposed framework. The experiment results show that our framework effectively identifies abnormal trajectories with 81.22% in terms of the F1-score.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.4
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• pp.409-421
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• 2023

In order to develop navigation systems, simulators that provide navigation sensors data are required. A trajectory generator that simulates vehicle motion is needed to generate navigation sensors data in the simulator. In this paper, a trajectory generator for evaluating navigation system performance is proposed. The proposed trajectory generator consists of two parts. The first part obtains parameters from the motion scenario file whereas the second part generates position, velocity, and attitude from the parameters. In the proposed trajectory generator six degrees of freedom, halt, climb, turn, accel turn, spiral, combined, and waypoint motions are given as basic motions with parameters. These motions can be combined to generate complex trajectories of the vehicle. Maximum acceleration and jerk for linear motion and maximum angular acceleration and velocity for rotational motion are considered to generate trajectories. In order to show the usefulness of the proposed trajectory generator, trajectories were generated from motion scenario files and the results were observed. The results show that the proposed trajectory generator can accurately simulate complex vehicle motions that can be used to evaluate navigation system performance.

• Advances in aircraft and spacecraft science
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• v.10 no.5
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• pp.473-494
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• 2023

This article introduces a formalism for the analysis of airplane trajectories on which the motion is determined by specifying the power of the engines. It explains a procedure to solve the equations of motion to obtain the value of the relevant flight parameters. It then enumerates the constraints that the dynamical abilities of the airplane impose on the amount of fuel used, the speed, the load factor, the lift coefficient, the positivity and upper boundedness of the power available. Examples of analysis are provided to illustrate the method proposed, with rectilinear and circular trajectories. Two very different types of airplanes are used in the examples: a Silver Fox-like small UAV and a common Cessna 182 Skylane.

• Smart Media Journal
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• v.13 no.2
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• pp.23-31
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• 2024

In precision cattle farm, reliably tracking the identity of each cattle is necessary. Effective tracking of cattle within farm environments presents a unique challenge, particularly with the need to minimize the occurrence of excessive tracking trajectories. To address this, we introduce a trajectory playback decision tree algorithm that reevaluates and cleans tracking results based on spatio-temporal relationships among trajectories. This approach considers trajectory as metadata, resulting in more realistic and accurate tracking outcomes. This algorithm showcases its robustness and capability through extensive comparisons with popular tracking models, consistently demonstrating the promotion of performance across various evaluation metrics that is HOTA, AssA, and IDF1 achieve 68.81%, 79.31%, and 84.81%.

• KIISE Transactions on Computing Practices
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• v.22 no.4
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• pp.189-194
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• 2016

A trajectory is the motion path of a moving object. The advances in IT have made it possible to collect an immeasurable amount of various type of trajectory data from a moving object using location detection devices like GPS. The trajectories of moving objects are widely used in many different fields of research, including the geographic information system (GIS) field. In the GIS field, several attempts have been made to automatically generate digital maps of roads by using the vehicle trajectory data. To achieve this goal, the method to cluster the trajectories on the same road is needed. Usually, the $Fr{\acute{e}}chet$ distance measure is used to calculate the distance between a pair of trajectories. However, the $Fr{\acute{e}}chet$ distance measure requires prolonged calculation time for a large amount of trajectories. In this paper, we presented a fast heuristic algorithm to distinguish whether the trajectories are in close distance or not using the discrete $Fr{\acute{e}}chet$ distance measure. This algorithm trades the accuracy of the resulting distance with decreased calculation time. By experiments, we showed that the algorithm could distinguish between the trajectory within 10 meters and the distant trajectory with 95% accuracy and, at worst, 65% of calculation reduction, as compared with the discrete $Fr{\acute{e}}chet$ distance.

• Journal of Korea Spatial Information System Society
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• v.8 no.2 s.17
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• pp.109-120
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• 2006

This paper addresses techniques for predicting a future path of an object moving on a road network. Most prior methods for future prediction mainly focus their attention on objects moving in Euclidean space. A variety of applications such as telematics, however, deal with objects that move only over road networks in most cases, thereby requiring an effective method of future prediction of moving objects on road networks. In this paper, we propose a novel method for predicting a future path of an object by analyzing past trajectories whose changing pattern is similar to that of a current trajectory of a query object. We devise a new function that measures a similarity between trajectories by reflecting the characteristics of road networks. By using this function, we predict a future path of a given moving object as follows: First, we search for candidate trajectories that contain subtrajectories similar to a given query trajectory by accessing past trajectories stored in moving object databases. Then, we predict a future path of a query object by analyzing the moving paths along with a current position to a destination of candidate trajectories thus retrieved. Also, we suggest a method that improves the accuracy of path prediction by regarding moving paths that have just small differences as the same group.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.6
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• pp.25-37
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• 2000

The trajectories of feature points can be defined by the correspondences between points in consecutive frames. The correspondence problem is known to be difficult to solve because false positives and false negatives almost always exist in real image sequences. In this paper, we propose a robust feature tracking algorithm considering incomplete trajectories such as entering and/or vanishing trajectories. The trajectories of feature points are determined by calculating the matching measure, which is defined as the minimum weighted Euclidean distance between two feature points. The weights are automatically updated in order to properly reflect the motion characteristics. We solve the correspondence problem as an optimal graph search problem, considering that the existence of false feature points may have serious effect on the correspondence search. The proposed algorithm finds a local optimal correspondence so that the effect of false feature point can be minimized in the decision process. The time complexity of the proposed graph search algorithm is given by O(mn) in the best case and O($m^2n$) in the worst case, where m and n arc the number of feature points in two consecutive frames. By considering false feature points and by properly reflecting motion characteristics, the proposed algorithm can find trajectories correctly and robustly, which has been shown by experimental results.

• Journal of Korean Home Economics Education Association
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• v.33 no.4
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• pp.31-48
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• 2021

The purpose of this study was to provide an understanding of the trajectories of multidimensional health among older adults, including depression, chronic diseases, and cognitive function. Data were drawn from the 1-6 waves of the Korean Longitudinal Study of Ageing(KLoSA), and a sample of 2,059 respondents aged 65 and older at baseline was used for the analyses. Latent growth curve models and growth mixture models were used to explore the changes in depression, chronic diseases, cognitive function, and heterogeneous trajectories among them. One-way ANOVAs with Scheffé post-hoc analysis and chi-square tests were used to find differences in sociodemographic characteristics, health behaviors, and life satisfaction across the latent trajectory classes. Latent growth curve models revealed that depressive symptoms and the number of chronic diseases increased over time, while cognitive function showed gradual decreases. Three heterogeneous patterns of multidimensional health trajectories were identified: normal aging, increase in chronic diseases, and chronic deterioration. Significant differences were observed in sociodemographic characteristics, health behaviors, and life satisfaction across the three latent classes. In particular, low educational attainment, household income, and life satisfaction were associated with the chronic deterioration class. Based on the findings, we discussed suggestions for health promotion education targeting older adults. This study also emphasizes the importance of home economics education in promoting health literacy across the life course.

• Korean Journal of Social Welfare Studies
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• v.41 no.1
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• pp.119-144
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• 2010

This study advances the knowledge of developmental patterns in affiliation with delinquent peers and delinquency during adolescence; data were obtained from waves 1-5 (2003-2007) of the Korea Youth Panel Survey. Semi-parametric group-based modeling (SGM) identified 3 affiliative trajectories of delinquent peers from age 13 to 16: rarely or never, persistently affiliative, and declining groups; and five developmental trajectories of delinquency: non-offending, late onset, low-level continuous, desisting, and chronic groups. A joint trajectory analysis predicted the membership of delinquency trajectories conditional on delinquent peer trajectories. Persistently affiliative group was more likely than others to follow chronic trajectory of delinquency; the rarely or never affiliative group was more likely to be non-offending. This study may help reconcile different theoretical models such as influence, selection, and enhancement models with respect to the role of delinquent peers in delinquency. The distinct theoretical models are equally valid, albeit each model pertains to a specific aspect of longitudinal patterns of affiliation with delinquent peers. Implications of this study for youth welfare were discussed to reduce increased risks for both affiliation with delinquent peers and delinquency.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.29A no.11
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• pp.40-48
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• 1992

The wave-kinetic numerical method is used in simulating the irradiance scintillations of optical wave through a two-dimensional random medium containing weak Gaussian fluctuations of the refractive index. The results are compared to existing analytical or numerical results. The wave-kinetic method is a phase-space ray-tracing method for certain key ray trajectories, and the irradiance is calculated by reconstructing the entire beam from these trajectories. The strength of the wave-kinetic method lies in the fact that it can be applied to any type of random media.