• Journal of Navigation and Port Research
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• v.41 no.4
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• pp.189-194
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• 2017

The Moving Search Radar System (MSRS) monitors sea areas by moving along the coast. Since the radar is initially aligned to the front of the vehicle, it is important to know the changes in the heading azimuth of the vehicle to quickly acquire the target azimuth from the radar after the MSRS has moved. The heading azimuth can be obtained using the gyro compass, the GPS compass or the electronic compass. The electronic compass is suitable for MSRS requiring fast maneuverability due to its small volume, short stabilization time and low price. However, using a geomagnetic sensor may result in an error due to the surrounding magnetic field. Errors can make early automatic tracking of the satellites difficult and can reduce the radar detection accuracy. Therefore, this paper proposes a method to automatically compensate for the error reflecting the correction value on the radar obtained by comparing the reference azimuth calculated by solving the geodesic inverse problem using two coordinates between the radar and the geostationary satellite with the actually-directed azimuth angle of the satellite antenna. The feasibility and convenience of the proposed method were verified by applying it to the MSRS in the field.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.4
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• pp.441-447
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• 2021

Construction sites are complex and dangerous because heavy equipment and workers perform various operations simultaneously within limited working areas. Solitary works of heavy equipment in complex job sites can cause fatal accidents, and thus they should interact with spotters and obtain information about surrounding environments during operations. Recently, many computer vision technologies have been developed to automatically monitor construction equipment and detect their interactions with other resources. However, previous methods did not take into account the interactions between equipment and spotters, which is crucial for identifying solitary works of heavy equipment. To address the drawback, this research develops a computer vision-based solitary work detection model that considers interactive operations between heavy equipment and spotters. To validate the proposed model, the research team performed experiments using image data collected from actual construction sites. The results showed that the model was able to detect workers and equipment with 83.4 % accuracy, classify workers and spotters with 84.2 % accuracy, and analyze the equipment-to-spotter interactions with 95.1 % accuracy. The findings of this study can be used to automate manual operation monitoring of heavy equipment and reduce the time and costs required for on-site safety management.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.205-218
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• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.1
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• pp.125-132
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• 2010

With recent advancement of remote sensing technology, a variety of data acquisition over the same area is achievable. An automated co-registration of heterogeneous airborne images is a critical step for change detection. This paper describes an automatic method for co-registration between digital image and existing building model. Optimal building models for co-registration purpose are extracted as primitives from existing building model database. A set of homologous features between straight lines extracted from aerial digital image and model primitive are computed based on geometric similarity function. With obtained homologous features, EO parameter is recomputed using least square method. The result shows that die suggested method automatically co-register two data set in a reliable manner.

• Convergence Security Journal
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• v.2 no.2
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• pp.39-47
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• 2002

To recently with the love-letter and Back Orifice the same Worm-virus, with the Trojan and the Linux-virus back against the new species virus which inside and outside of the country to increase tendency the malignant new species virus which is the possibility of decreasing the damage which is enormous in the object appears and to follow a same network coat large scale PC is being quicker, it disposes spontaneously to respect, applied an artificial intelligence technique the research against the next generation malignant computer virus of new form is demanded. Will reach and to respect it analyzes the digital immunity system of the automatic detection which is quick against the next generation malignant virus which had become unconfirmed and the foreign countries which has an removal function.

• Journal of the Korean earth science society
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• v.32 no.6
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• pp.595-601
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• 2011

In this paper, I propose the algorithm that the location of a magnetic dipole can be detected from the magnetic gradient tensor. I induce the location vector of a vertically magnetizated dipole from the magnetic gradient tensor. Deficit of magnetic moment of magnetic dipole makes the induced location information incomplete. However, if the observation of magnetic gradient tensor would be collected on more points, the algorithm is able to catch the location of the magnetic dipole by clustering the solution of the proposed algorithm. For example, I show that the synthetic case of borehole observation of magnetic gradient tensor can find the source location successively by picking common solution area.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.11
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• pp.1063-1069
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• 2011

NCTR(Non-Cooperative Target Recognition) and ATR(Automatic Target Recognition) are methodologies to identify military targets using radar, optical, and infrared images. Among them, a strategy to recognize ground targets using synthetic aperature radar(SAR) images is called SAR ATR. In general, SAR ATR consists of three sequential stages: detection, discrimination and classification. In this paper, a modification of the polar mapping classifier(PMC) to identify inverse SAR(ISAR) images has been made in order to apply it to SAR ATR. In addition, a preprocessing scheme can mitigate the effect from the clutter, and information on the shadow is employed to improve the classification accuracy.

• Korean Journal of Optics and Photonics
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• v.26 no.4
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• pp.187-194
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• 2015

A system of infrared camera optics with wide field of view and anamorphic lenses is proposed, and its validity verified through manufacture. The infrared camera produced provides a wide field of view of over 100 degrees in the horizontal direction, and an even greater magnification in the vertical direction. As a result, the system can have a wider surveillance range and improved detection ability at the same time. In addition, a new optomechanical automatic athermalization mechanism is proposed and applied to the infrared camera. Its performance and utility is proved through testing.

• Proceedings of KOSOMES biannual meeting
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• 2005.05a
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• pp.209-213
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• 2005

The current AIS network and. system are run on a full scale with 22 ground stations and. 11 operational systems, completing a nation-wide, integrated network However, currently it needs to manage sea traffic by linking AIS to VIS which 1vs a limited service area due to restricted radar detection zones in harbors or coastal areas. Accordingly this study analyzes the current status of the AIS system and. proposes technological and. operational improvement plan of the current AIS system through investigating the actual conditions of the AIS system and. its operations.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.69-74
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• 2009

Abandoned objects should be treated as possibly dangerous things for public areas until they turn out to be safe because explosive material or chemical substance is intentionally contained in them for public terrors. For large public areas such as airports or train stations, there are limits in man-power for security staffs to check all the monitors for covering the entire area under surveillance. This is the basic motivation of developing the automatic detection system for dangerous abandoned objects based on vision technology. In this research, well-known DBE is applied to stably extract background images and the HOG algorithm is adapted to discriminate between human and stuff for object classification. To show the effectiveness of the proposed system, experiments are carried out in detecting intrusion for a forbidden area and alarming for abandoned objects in a room under surveillance.