• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.3
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• pp.1-13
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• 2024

This paper proposes a cost-effective system design and user-friendly approach for the key technological elements necessary to configure an autonomous mobile robot. To implement a high-precision positioning system using an autonomous mobile robot, we established a Linux-based VRS (virtual reference station)-RTK (real-time kinematic) GNSS (global navigation satellite system) system with NTRIP (Network Transport of RTCM via Internet Protocol) client functionality. Notably, we reduced the construction cost of the GNSS positioning system by performing dynamic location analysis of the established system, without utilizing an RTK replay system. Dynamic location analysis involves sampling each point during the trajectory following of the autonomous mobile robot and comparing the location precision with ground-truth points. The proposed system ensures high positioning performance with fast sampling times and suggests a GPS waypoint system for user convenience. The centimeter-level precision GNSS information is provided at a 30Hz sampling rate, and the dead reckoning function ensures valid information even when passing through tall buildings and dense forests. The horizontal position error measured through the proposed system is 6.7cm, demonstrating a highly precise dynamic location measurement error within 10cm. The VRS network-RTK Linux system, which provides precise dynamic location information at a high sampling rate, supports a GPS waypoint planner function for user convenience, enabling easy destination setting based on GPS information.

• Journal of Multimedia Information System
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• v.7 no.2
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• pp.147-156
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• 2020

The author Nakajima was involved in the field of disaster communications and emergency medical care as guest research scientist at the Fukushima Nuclear Accident Independent Investigation Commission established by the National Diet of Japan and reviewer of the Commission's report, and Kurokawa was the chairman of this Commission. Looking back over a decade, we are on the liability issue of bureaucrats and telecom operators, so it's becoming clear what was hidden at the time. The battery of NTT DoCoMo's mobile phone repeaters had a capacity of only about 24 hours, and communication failures increased after one day. The Government also failed to issue an announcement of "Vent from reactor" under the Telecommunications Act Article No. 129. This mistake lost the opportunity to use the third-party telecommunications (e.g. taxi radios). Furthermore, as a result of LASCOM (telecommunications satellite network for local governments via GEO) and a variety of unexpected communication failures, the evacuation order "Escape!" could not be notified to the general public well. As a result, the general public was exposed to unnecessary radiation exposure. Such bureaucratic slow action in emergencies is common in the response to the 2020 coronavirus.

• Journal of Navigation and Port Research
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• v.27 no.6
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• pp.693-700
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• 2003

Universal AIS, which has been adopted officially for automatic identification systems among regulated ships by SOLAS, should be installed, for example, on all passenger ships over 300 tons engaged in international voyage and over 500 tons in domestic voyage, sequentially from 2002 to 2004. We must not overlook the fact than-ruled regions by regional authorities in the case of VTS. Actually a major portion of accidents have happened in small vessels like fishing vessels. However, they are not equipped with automatic identification tools, due to the high costs of the equipment for identifying purposes, as well as the absence of regulation In this paper, we researched the alternative of automatic identification for small vessel instead of universal AIS. We analyzed the requirement of automatic identification for small vessel about wireless communication method, traffic volume, etc. We proposed the identification system for small vessels in local areas and developed the Local Vessel Identification System (LVIS) interoperable with universal AIS using a PDA platform and wireless network.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.1
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• pp.118-129
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• 2011

This Study has developed the information system of the rivers based on 3D image GIS by converging the latest information technology of GIS(Geographic Information System), RS(Remote Sensing), GNSS(Global Navigation Satellite System), aerial laser survey(LiDAR) with real time network technology in order to understand the current situation of all the four major rivers and support the administrative management system. The said information system acquires the high resolution aerial photographs of 25cm, aerial laser survey and water depth surveying data to express precise space information on the whole Youngsan River which is the leading project site out of the four river sites. Monitoring the site is made available on the transporting means such as a helicopter, boat or a bus in connection with locational coordinate tracking skill for the moving objects in real time using GNSS. It makes monitoring all the information on the four river job sites available at a glance, which can obtain the reliability of the people to such vast areas along with enhancing the recognition of the people by publicity of four Rivers Revitalizing Project and reports thereof.

• 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.

• Journal of KIISE:Information Networking
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• v.34 no.6
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• pp.466-474
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• 2007

Asymmetric networks that the downlink bandwidth is larger than the uplink bandwidth may cause the degradation of the TCP performance due to the uplink congestion. In order to solve this problem, this paper designs and implements the Dynamic Segment Size Control mechanism which offers a suitable segment size for current networks. The proposed mechanism does not require any changes in customer premises but suppress the number of ACKs using segment reassembly technique to avoid the uplink congestion. The gateway which adapted the Dynamic Segment Size Control mechanism, detects the uplink congestion condition and dynamically measures the bandwidth asymmetric ratio and the packet loss ratio. The gateway reassembles some of segments received from the server into a large segment and transmits it to the client. This reduces the number of corresponding ACKs. In this mechanism, the SACK option is used when occurs the bit error during the transmission. Based on the simulation in the GEO satellite network environment, we analyzed the performance of the Dynamic Segment Size Control mechanism.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.183-192
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• 2023

The remotely sensed data, such as satellite imagery and aerial photos, can be used to extract and detect some objects in the image through image interpretation and processing techniques. Significantly, the possibility for utilizing digital map updating and land monitoring has been increased through automatic object detection since spatial resolution of remotely sensed data has improved and technologies about deep learning have been developed. In this paper, we tried to extract plastic greenhouses into aerial orthophotos by using fully convolutional densely connected convolutional network (FC-DenseNet), one of the representative deep learning models for semantic segmentation. Then, a quantitative analysis of extraction results had performed. Using the farm map of the Ministry of Agriculture, Food and Rural Affairsin Korea, training data was generated by labeling plastic greenhouses into Damyang and Miryang areas. And then, FC-DenseNet was trained through a training dataset. To apply the deep learning model in the remotely sensed imagery, instance norm, which can maintain the spectral characteristics of bands, was used as normalization. In addition, optimal weights for each band were determined by adding attention modules in the deep learning model. In the experiments, it was found that a deep learning model can extract plastic greenhouses. These results can be applied to digital map updating of Farm-map and landcover maps.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1605-1613
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• 2023

The physical properties of the ocean interior are determined by temperature and salinity. To observe them, we rely on satellite observations for broad regions of oceans. However, the satellite for salinity measurement, Soil Moisture Active Passive (SMAP), has low temporal and spatial resolutions; thus, more is needed to resolve the fast-changing coastal environment. To overcome these limitations, the algorithm to use the Geostationary Ocean Color Imager-II (GOCI-II) of the Geo-Kompsat-2B (GK-2B) was developed as the inputs for a Multi-layer Perceptron Neural Network (MPNN). The result shows that coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (RRMSE) between GOCI-II based sea surface salinity (SSS) (GOCI-II SSS) and SMAP was 0.94, 0.58 psu, and 1.87%, respectively. Furthermore, the spatial variation of GOCI-II SSS was also very uniform, with over 0.8 of R² and less than 1 psu of RMSE. In addition, GOCI-II SSS was also compared with SSS of Ieodo Ocean Research Station (I-ORS), suggesting that the result was slightly low, which was further analyzed for the following reasons. We further illustrated the valuable information of high spatial and temporal variation of GOCI-II SSS to analyze SSS variation by the 11th typhoon, Hinnamnor, in 2022. We used the mean and standard deviation (STD) of one day of GOCI-II SSS, revealing the high spatial and temporal changes. Thus, this study will shed light on the research for monitoring the highly changing marine environment.