• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.1
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• pp.81-89
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• 2017

In this paper, we propose a gait pattern recognition method for intelligent prosthesis that enables walking in various environments of femoral amputees. The proposed gait mode changing method is a single sensor based algorithm which can discriminate gait surface and gait phase using only strain gauges sensor, and it is designed to simplify the algorithm based on multiple sensors of existing intelligent prosthesis and to reduce cost of prosthesis system. For the recognition algorithm, we analyzed characteristics of the ground reaction force generated during gait of normal person and defined gait step segmentation and gait detection condition, A gait analyzer was constructed for the gait experiment in the environment similar to the femoral amputee. The validity of the paper was verified through the defined detection conditions and fabricated instruments. The accuracy of the algorithm based on the single sensor was 95%. Based on the proposed single sensor-based algorithm, it is considered that the intelligent prosthesis system can be made inexpensive, and the user can directly grasp the state of the walking surface and shift the walking mode. It is confirmed that it is possible to change the automatic walking mode to switch the walking mode that is suitable for the walking mode.

• Journal of the Korean GEO-environmental Society
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• v.15 no.6
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• pp.57-65
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• 2014

Recently, Construction business is changing from on the ground to underground space because of deficit of developing space, creation of green space and of incremental of land compensation expenses. Meanwhile, 3D Topographic, Marine and Cadastral maps need to have Spatial Interrelation. Also, understanding of the information is also needed. Spatial information object registration system is impossible to contact and understanding intelligence mutually because the former one is managed as automatic ID system. Therefore, 3D Object information ID System of underground space is managed based on Object Identifier. Construction of Spatial information integration ID System is required and it will offer Division Code (Ground, Index, Underground) and depth information. We are defined and classified Under Spatial Information in this paper. Moreover, we developed the integration ID System based on UFID for cadastral information Construction. We supposed underground spatial information DB Construction and a developed the way of exploiting 3D cadastral information system through the study. The research result will be the base data of Standard ID system, DB Construction and system Development of National spatial data which is considered together with spatial interrelation.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1463-1478
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• 2022

Maritime transport accounts for 99.7% of the exports and imports of the Republic of Korea; therefore, developing a vessel monitoring system for efficient operation is of significant interest. Several studies have focused on tracking and monitoring vessel movements based on automatic identification system (AIS) data; however, ships without AIS have limited monitoring and tracking ability. High-resolution optical satellite images can provide the missing layer of information in AIS-based monitoring systems because they can identify non-AIS vessels and small ships over a wide range. Therefore, it is necessary to investigate vessel monitoring and small vessel classification systems using high-resolution optical satellite images. This study examined the possibility of developing ship monitoring systems using Compact Advanced Satellite 500-1 (CAS500-1) satellite images by first training a deep learning model using satellite image data and then performing detection in other images. To determine the effectiveness of the proposed method, the learning data was acquired from ships in the Yellow Sea and its major ports, and the detection model was established using the You Only Look Once (YOLO) algorithm. The ship detection performance was evaluated for a domestic and an international port. The results obtained using the detection model in ships in the anchorage and berth areas were compared with the ship classification information obtained using AIS, and an accuracy of 85.5% and 70% was achieved using domestic and international classification models, respectively. The results indicate that high-resolution satellite images can be used in mooring ships for vessel monitoring. The developed approach can potentially be used in vessel tracking and monitoring systems at major ports around the world if the accuracy of the detection model is improved through continuous learning data construction.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.618-628
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• 2020

The purpose of this paper is to propose a new security orchestration service model by combining various security solutions that have been introduced and operated individually as a basis for cyber security framework. At present, in order to respond to various and intelligent cyber attacks, various single security devices and SIEM and AI solutions that integrate and manage them have been built. In addition, a cyber security framework and a security control center were opened for systematic prevention and response. However, due to the document-oriented cybersecurity framework and limited security personnel, the reality is that it is difficult to escape from the control form of fragmentary infringement response of important detection events of TMS / IPS. To improve these problems, based on the model of this paper, select the targets to be protected through work characteristics and vulnerable asset identification, and then collect logs with SIEM. Based on asset information, we established proactive methods and three detection strategies through threat information. AI and SIEM are used to quickly determine whether an attack has occurred, and an automatic blocking function is linked to the firewall and IPS. In addition, through the automatic learning of TMS / IPS detection events through machine learning supervised learning, we improved the efficiency of control work and established a threat hunting work system centered on big data analysis through machine learning unsupervised learning results.

• Journal of Environmental Science International
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• v.24 no.5
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• pp.657-670
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• 2015

Visibility and Automatic Weather System(AWS) data near Nakdong river were analyzed to characterize fog formation during 2012-2013. The temperature was lower than its nearby city - Daegu, whereas the humidity was higher than the city. 157 fog events were observed in total during the 2 year period. About 65% of the events occurred in fall (September, October, and November) followed by winter, summer, and spring. 94 early morning fog events of longer than 30 minutes occurred when south westerly wind speed was lower than 2 m/s. During these events, the water temperature was highest followed by soil surface and air temperatures due to the advection of cold and humid air from nearby hill. The observed fog events were categorized using a fog-type classification algorithm, which used surface cooling, wind speed threshold, rate of change of air temperature and dew point temperature. As a result, frontal fog observed 6 times, radiation 4, advection 13, and evaporation 66. The evaporation fog in the study area lasted longer than other reports. It is due to the interactions of cold air drainage flow and warm surface in addition to the evaporation from the water surface. In particular, more than 60% of the evaporation fog events were accompanied with cold air flows over the wet and warm surface. Therefore, it is needed for the identification of the inland fog mechanism to evaluate the impacts of nearby topography and land cover as well as water body.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.469-473
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• 2006

Nowadays, to realize ubiquitous computing environment, many research activities have been going on within various kinds of research domains including automatic identification, sensor network, home network, telematics and so on. Especially, RFID middle-ware that supports the aggregation of RFID tag data, control and management, and the integration with legacy systems has recently gained a lot of attention. Meanwhile, EPCglobal defined an ALE(Application Level Events) standard specification, which exists between RFID readers and applications, and substitutes the previous systems called Savant. In the specification internal structures and implementation technologies of ALE are not mentioned and only external interfaces are defined. This approach easies the verification of standard compliance and inter-operability of the layer. In this paper, we present the design of ALE-compliant RFID middle-ware systems that process RFID tag data efficiently.

• Proceedings of KOSOMES biannual meeting
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• 2006.11a
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• pp.217-219
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• 2006

This study focused an water quality response to land-based pollution loads and the appropriate pollutant load reduction in Chinhae Bay using an eco-hydrodynamic model. Land-based discharge foam urban areas, industrial complex and sewage treatment plant was the greatest contributor to cause red-tide blooms and summer hypoxia. Tidal currents velocity af the ebb tide was about 10 cm/s stronger than that of the flood tide. A residual current was simulated to. have a slightly complicated pattern with ranging from 0.1 to. 2.7 cm/s. In Masan Bay, pollutant materials cannot flaw from the inner to the outer bay easily because af residual currents flaw southward at surface and northward at the bottom. The simulation results of COD distribution showedhigh concentrations aver 3 mg/L in the inner part of Masan Bay related pollutant discharge, and charge, and lower levels less than 1.5mg/L in the central part of Chinhae Bay. For improvement water quality in Chinhae Bay, it is necessary to reduce the organic and inorganic loads from paint sources by mare than 50% and ameliorate severe polluted sediment.

• Journal of the Society of Naval Architects of Korea
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• v.54 no.6
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• pp.501-514
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• 2017

In the previous research, a study was carried out to estimate the actual performance such as the propeller Revolution Per Minute (RPM) and engine power of a Liquefied Natural Gas Carrier (LNGC) using the full-scale measurement data. After the predicted RPM and engine power were verified by comparing those with the measured values, the suggested method was regarded to be acceptable. However, there was a limitation to apply the method on the prediction of the RPM and engine power of a ship. Since the information of route, speed, and environmental conditions required for estimating the RPM and engine power is generally regarded as the intellectual property of a shipping company, it is difficult to secure the information on a shipyard. In this paper, the RPM and engine power of the 151K LNGC was estimated using the combination of Automatic Identification System (AIS) and European Centre for Medium-Range Weather Forecasts (ECMWF) database in order to replace the full-scale measurement. The simulation approach, which was suggested in the previous research, was identically applied to the prediction of RPM and engine power. After the results based on the AIS and ECMWF database were compared with those obtained from the full-scale measurement data, the feasibility was briefly reviewed.

• Journal of Korea Port Economic Association
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• v.34 no.1
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• pp.65-80
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• 2018

This paper attempts to estimate GHG emissions, primarily $CO_2$ ship emissions, at the port of Incheon in October 2014. This study employed a bottom-up approach based on Automatic Identification System (AIS) data to estimate the total amount of fuel consumption and the total amount of $CO_2$ emission produced as a result of fuel combustion. Using a sample of 330 ships operating at the port of Incheon in Korea, the total amount of $CO_2$ gases emitted from ships in October 2014 were estimated to be 164693.06 tons, with estimated total fuel consumption of 51953.64 tons. General cargo ships were most common type of ships, but they were less polluting compared to passenger ships. The detailed emission estimates by ship type revealed that passenger ships were the most polluting ships (81409.6 tons of emissions), followed by tugboats (37248.4 tons), cargo ships (32154.6 tons), ships used for other activities (9039.1 tons), chemical tankers (4027.06 tons), and fishing ships (814.048 tons), respectively.

• Journal of Korean Society of Forest Science
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• v.109 no.1
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• pp.23-30
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• 2020

The use of increased computing power, machine learning, and deep learning techniques have dramatically increased in various sectors. In particular, image detection algorithms are broadly used in forestry and remote sensing areas to identify forest types and tree species. However, in South Korea, machine learning has rarely, if ever, been applied in forestry image detection, especially to classify tree species. This study integrates the application of machine learning and forest image detection; specifically, we compared the ability of two machine learning data collection methods, namely image data captured by forest experts (D1) and web-crawling (D2), to automate the classification of five trees species. In addition, two methods of characterization to train/test the system were investigated. The results indicated a significant difference in classification accuracy between D1 and D2: the classification accuracy of D1 was higher than that of D2. In order to increase the classification accuracy of D2, additional data filtering techniques were required to reduce the noise of uncensored image data.