• Journal of Advanced Marine Engineering and Technology
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• v.32 no.1
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• pp.192-199
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• 2008

In order for a mobile robot to move under unknown or uncertain environment. it is very important to collect environmental information. This paper suggests a traveling algorithm which leads to the map building algorithm and the $A^*$ algorithm under the assumption that environmental information should already be collected. In order to apply the proposed traveling algorithm to a real mobile robot. this paper additionally discusses a path amendment algorithm. For the purpose of verifying the proposed algorithms, several simulations are executed based on a UI host program-based simulation interface and an experiment is executed using a mobile robot under a real unknown environment.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.2
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• pp.351-358
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• 2008

This paper introduces a map building algorithm which can collect environmental information using ultrasonic sensors. And also this paper discusses a traveling algorithm using environmental information which leads to the map building algorithm. In order to accomplish the proposed traveling algorithm, this paper additionally discusses a path revision algorithm. For verifying the proposed algorithms, several experiments are executed using a mobile robot physically designed in this paper. The conclusion is that the proposed algorithm is very effective and is applicable to mobile robots especially requiring a low-cost environmental information.

• Spatial Information Research
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• v.19 no.6
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• pp.111-121
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• 2011

In this paper, we proposes a sensor node positioning algorithm that utilizes the geo-spatial elements and considers the factors to represent the propagation loss generated by the various obstacles in the urban wireless environments. First, we measures the propagation loss about the radio frequencies in major road of the urban, and defines the correlation between the measured loss and the environment information for the road and its surrounding get from Urban GIS. Secondly, through the utilization of the loss-environment correlation, we describes the detailed instruction for requiring the radio coverage decision and deploy system implementation for the wireless sensor node in urban. By the consideration of interference factor by the building and the linear structure of road, we can evaluate the path loss below 5dB RMS error. And, we proposes the way to revise the sensor node deployment based on the corelation and the measured path loss.

• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.235-243
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• 2023

Due to the revision of the River Act and the enactment of the Act on the Investigation, Planning, and Management of Water Resources, a regular bed change survey has become mandatory and a system is being prepared such that local governments can manage water resources in a planned manner. Since the topography of a bed cannot be measured directly, it is indirectly measured via contact-type depth measurements such as level survey or using an echo sounder, which features a low spatial resolution and does not allow continuous surveying owing to constraints in data acquisition. Therefore, a depth measurement method using remote sensing-LiDAR or hyperspectral imaging-has recently been developed, which allows a wider area survey than the contact-type method as it acquires hyperspectral images from a lightweight hyperspectral sensor mounted on a frequently operating drone and by applying the optimal bandwidth ratio search algorithm to estimate the depth. In the existing hyperspectral remote sensing technique, specific physical quantities are analyzed after matching the hyperspectral image acquired by the drone's path to the image of a surface unit. Previous studies focus primarily on the application of this technology to measure the bathymetry of sandy rivers, whereas bed materials are rarely evaluated. In this study, the existing hyperspectral image-based water depth estimation technique is applied to rivers with vegetation, whereas spatio-temporal hyperspectral imaging and cross-sectional hyperspectral imaging are performed for two cases in the same area before and after vegetation is removed. The result shows that the water depth estimation in the absence of vegetation is more accurate, and in the presence of vegetation, the water depth is estimated by recognizing the height of vegetation as the bottom. In addition, highly accurate water depth estimation is achieved not only in conventional cross-sectional hyperspectral imaging, but also in spatio-temporal hyperspectral imaging. As such, the possibility of monitoring bed fluctuations (water depth fluctuation) using spatio-temporal hyperspectral imaging is confirmed.