• Korean Journal of Child Studies
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• v.25 no.6
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• pp.1-13
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• 2004

The purposes of this study were to investigate whether there were differences in the young children's abilities to use spatial coordinates and to represent spatial locations by children's age and sex, and to examine the relationship between these two abilities. It also explored whether the young children could use coordinates as the frames of reference for representing spatial locations. Seventy 5- and 6-year-old children from two kindergartens in Seoul and in Bucheon participated in this study. Results indicated that there were statistically significant differences between age groups on the children's ability to use spatial coordinates and to represent spatial locations. However, there were no significant differences between boys and girls on these two abilities. A positive correlation was found between theses two abilities of using spatial coordinates and representing spatial locations. Most of the young children used landmarks as the frames of reference to represent spatial locations while some of the children were partially able to use spatial coordinates. Twenty percent of 6-year-old children were fully able to use spatial coordinates as the frames of reference to represent spatial locations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1726-1748
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• 2024

Trajectory planning is vital for autonomous systems like robotics and UAVs, as it determines optimal, safe paths considering physical limitations, environmental factors, and agent interactions. Recent advancements in trajectory planning and future location prediction stem from rapid progress in machine learning and optimization algorithms. In this paper, we proposed a novel framework for Spatial-temporal transformer-based feed-forward neural networks (STTFFNs). From the traffic flow local area point of view, skip-gram model is trained on trajectory data to generate embeddings that capture the high-level features of different trajectories. These embeddings can then be used as input to a transformer-based trajectory planning model, which can generate trajectories for new objects based on the embeddings of similar trajectories in the training data. In the next step, distant regions, we embedded feedforward network is responsible for generating the distant trajectories by taking as input a set of features that represent the object's current state and historical data. One advantage of using feedforward networks for distant trajectory planning is their ability to capture long-term dependencies in the data. In the final step of forecasting for future locations, the encoder and decoder are crucial parts of the proposed technique. Spatial destinations are encoded utilizing location-based social networks(LBSN) based on visiting semantic locations. The model has been specially trained to forecast future locations using precise longitude and latitude values. Following rigorous testing on two real-world datasets, Porto and Manhattan, it was discovered that the model outperformed a prediction accuracy of 8.7% previous state-of-the-art methods.