• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• 재활복지
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• v.14 no.3
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• pp.225-256
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• 2010

This study aims to provide the basic data of the rehabilitation program for the schoolchild with intellectual disability by designing new framework of the features of postural control for the schoolchild with intellectual disability. For this, the study investigated what sensations the schoolchild are using to maintain posture by selectively or synthetically applying vision, vestibular sensation and somato-sensation, and how the coordinative sensory system of the schoolchild is responding to any sway referenced sensory stimulus. The study intended to prove the limitation of motor system in estimating the postural stability by providing the cognitive motor task, and provided the features of postural control of the schoolchild with intellectual disability by measuring the onset times and orders of muscle contraction of neuron-muscle when there is a postural control taking place due to the exterior disturbance. Furthermore, by comparatively analyzing the difference between the normal schoolchild and the intellectually disabled schoolchild, this study provided an optimal direction for treatment planning when the rehabilitation program is applied in the postural control ability training program for the schoolchild with intellectual disability. Taking gender and age into consideration, 52 schoolchild including 26 normal schoolchild and 26 intellectually disabled schoolchild were selected. To measure the features of postural control, CTSIB test, and postural control strategy test were conducted. The result of experiment is as followed. First, the schoolchild with intellectual disability showed different feature in using sensory system to control posture. The normal schoolchild tended to depend on somato-sensory or vision, and showed a stable postural control toward a sway referenced stimulus on somato-sensory system. The schoolchild with intellectual disability tended to use somato-sensory or vision, and showed a very instable postural control toward a sway referenced vision or a sway referenced stimulus on somato-sensory system. In sensory analysis, the schoolchild with intellectual disability showed lower level of proficiency in somato-sensation percentile, vision percentile and vestibular sensation percentile compare to the normal schoolchild. Second, as for the onset times and orders of muscle contraction for strategies of postural control when there is an exterior physical stimulus, the schoolchild with intellectual disability showed a relatively delayed onset time of muscle control, and it was specially greater when the perturbation is from backward. As for the onset orders of muscle contraction, it started from muscles near coax then moved to the muscles near ankle joint, and the numbers and kinds of muscles involved were greater than the normal schoolchild. The normal schoolchild showed a fast muscle contracting reaction from every direction after the perturbation stimulus, and the contraction started from the muscles near the ankle joint and expanded to the muscles near coax. From the results of the experiments, the special feature of the postural control of the schoolchild with intellectual disability is that they have a higher dependence on vision in sensory system, and there was no appropriate integration of swayed sensation observed in upper level of central nerve system. In the motor system, the onset time of muscle contraction for postural control was delayed, and it proceeded in reversed order of the normal schoolchild. Therefore, when use the clinical physical therapy to improve the postural control ability, various sensations should be provided and should train the schoolchild to efficiently use the provided sensations and use the sensory experience recorded in upper level of central nerve system to improve postural control ability. At the same time, a treatment program that can improve the processing ability of central nerve system through meaningful activities with organizing and planning adapting reaction should be provided. Also, a proprioceptive motor control training program that can induce faster muscle contraction reaction and more efficient onset orders from muscularskeletal system is need to be provided as well.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1097-1109
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• 2023

Urban is an area where small-scale changes to individual buildings occur frequently. An existing urban building database requires periodic updating to increase its usability. However, there are limitations in data collection for building changes over a wide urban. In this study, we check the possibility of detecting building changes and updating a building database by using satellite images that can capture a wide urban region by a single image. For this purpose, building areas in a satellite image are first extracted by projecting 3D coordinates of building corners available in a building database onto the image. Building areas are then divided into roof and facade areas. By comparing textures of the roof areas projected, building changes such as height change or building removal can be detected. New height values are estimated by adjusting building heights until projected roofs align to actual roofs observed in the image. If the projected image appeared in the image while no building is observed, it corresponds to a demolished building. By checking buildings in the original image whose roofs and facades areas are not projected, new buildings are identified. Based on these results, the building database is updated by the three categories of height update, building deletion, or new building creation. This method was tested with a KOMPSAT-3A image over Incheon Metropolitan City and Incheon building database available in public. Building change detection and building database update was carried out. Updated building corners were then projected to another KOMPSAT-3 image. It was confirmed that building areas projected by updated building information agreed with actual buildings in the image very well. Through this study, the possibility of semi-automatic building change detection and building database update based on single satellite image was confirmed. In the future, follow-up research is needed on technology to enhance computational automation of the proposed method.