• The Journal of Korea Robotics Society
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• v.12 no.3
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• pp.249-257
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• 2017

This paper proposes a method to segment urban scenes semantically based on location prior information. Since major scene elements in urban environments such as roads, buildings, and vehicles are often located at specific locations, using the location prior information of these elements can improve the segmentation performance. The location priors are defined in special 2D coordinates, referred to as road-normal coordinates, which are perpendicular to the orientation of the road. With the help of depth information to each element, all the possible pixels in the image are projected into these coordinates and the learned prior information is applied to those pixels. The proposed location prior can be modeled by defining a unary potential of a conditional random field (CRF) as a sum of two sub-potentials: an appearance feature-based potential and a location potential. The proposed method was validated using publicly available KITTI dataset, which has urban images and corresponding 3D depth measurements.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.996-1002
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• 2015

In this paper, we present an effective system for the 3D scene labeling of objects from RGB-D videos. Our system uses a Markov Random Field (MRF) over a voxel representation of the 3D scene. In order to estimate the correct label of each voxel, the probabilistic graphical model integrates both scores from sliding window-based object detectors and also from object location prior maps. Both the object detectors and the location prior maps are pre-trained from manually labeled RGB-D images. Additionally, the model integrates the scores from considering the geometric constraints between adjacent voxels in the label estimation. We show excellent experimental results for the RGB-D Scenes Dataset built by the University of Washington, in which each indoor scene contains tabletop objects.

• Journal of Auto-vehicle Safety Association
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• v.16 no.2
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• pp.20-26
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• 2024

The delivery using drones has been attracting attention because it can innovatively reduce the delivery time from the time of order to completion of delivery compared to the current delivery system, and there have been pilot projects conducted for safe drone delivery. However, the current drone delivery system has the disadvantage of limiting the operational efficiency offered by fully autonomous delivery drones in that drones mainly deliver goods to pre-set landing sites or delivery bases, and the final delivery is still made by humans. In this paper, to overcome these limitations, we propose obstacle detection and landing site selection algorithm based on a vision sensor that enables safe drone landing at the delivery location of the product orderer, and experimentally prove the possibility of station-to-door delivery. The proposed algorithm forms a 3D map of point cloud based on simultaneous localization and mapping (SLAM) technology and presents a grid segmentation technique, allowing drones to stably find a landing site even in places without prior information. We aims to verify the performance of the proposed algorithm through streaming data received from the drone.

• Journal of Korean Society for Geospatial Information Science
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• v.12 no.1 s.28
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• pp.69-77
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• 2004

Since there are a lot of changes in climate on domestic and natural disasters owing to the disturbance-development of the land, damages of properties and human life frequently occur due to the coastal floodings. Accordingly, it is necessary to find the area where the danger of flooding coasts is relatively high and to inform resident the characteristics of the area As a part of preventive land management to minimize the flooding damages of the coastal area, this study suggested the generation of the coastal flood hazard map that provides detailed information such as refuge path, a place of refuge, and the location of medical supplies, food, and main rescue equipment, etc. This study selected the southern region of Daebu-do as an exemplary area, conducted a document study to establish GIS data, secured pre-structured data, and suggested the method of establishing GIS data fit to the study area. In particular, it emphasized the efficient construction of the geographical spatial data that were accurate, economic, objective, and realistic in supporting the modeling to predict the flooding zone. The specific type of established database was divided into flooding risk area, flooding warning area, and flooding-volume area. The prototype of coastal flood hazard map can be widely used for efficient disaster management. Furthermore, it is considered that the map could be applied for arousing residents' attentions to the flooding, prior education, and local governments' management actions against the danger of flooding.

• The Korean Journal of Nuclear Medicine
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• v.25 no.2
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• pp.165-176
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• 1991

The purpose of this study was to assess the ability of quantitative Tl-201 tomography to identify and localize coronary artery disease (CAD). The study population consisted of 41 patients (31 males, 10 females; mean age $55{\pm}7$ yr) including 14 with prior myocardial infarction who underwent both exercise Tl-201 myocardium SPECT and coronary angiography for the evaluation of chest pain. From the short axis and vertical long axis tomograms, stress extent polar maps were generated by Cedars-Sinai Medical Center program, and the % stress defect extent (SDE) was quantified for each coronary artery territory. For the purpose of this study, the coronary circulation was divided into 6 arterial segments, and the "myocardial ischemic score" (MIS) was calculated from the coronary angiogram. Sensitivity for the detection of CAD ($\geq50%$ coronary stenosis by angiography) by angiography) by stress extent polar map was 95% in single vessel disease, and 100% in double and triple vessel deseases. Overall sensitivity was 97%. Sensitivity and specificity for the detection of individual diseased vessels were, respectively, 87% and 90% for the left anterior descending artery (LAD), 36% and 93% for the left circumflex artery (LCX), and 71% and 70% for the right coronary artery (RCA). Concordance for the detection of individual diseased vessels between the coronary angiography and stress polar map was fair for the LAD (kappa=0.70), and RCA (kappa=0.41) lesions, whereas it was poor for the LCX lesions (kappa : 0.32). There were siginificant correlations between the MIS and SDE in LAD (rs=0.56, p=0.0027), and RCA territory (rs=0.60, p=0.0094). No significant correlation was found in LCX territory. When total vascular territories were combined, there was a significant correlation between the MIS and SDE (rs=0.42, p=0.0116). In conclusion, the quantitative analysis of Tl-201 tomograms appears to be accurate for determining the presence and location of CAD.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_1
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• pp.483-491
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• 2013

By a development of car navigation systems and mobile or positioning technology, it increases interest in location based services, especially pedestrian navigation systems. Updating of digital maps is important because digital maps are mass data and required to short updating cycle. In this paper, we proposed change detection for different network data-sets based on areal feature matching. Prior to change detection, we defined type of updating between different network data-sets. Next, we transformed road lines into areal features(block) that are surrounded by them and calculated a shape similarity between blocks in different data-sets. Blocks that a shape similarity is more than 0.6 are selected candidate block pairs. Secondly, we detected changed-block pairs by bipartite graph clustering or properties of a concave polygon according to types of updating, and calculated Fr$\acute{e}$chet distance between segments within the block or forming it. At this time, road segments of KAIS map that Fr$\acute{e}$chet distance is more than 50 are extracted as updating road features. As a result of accuracy evaluation, a value of detection rate appears high at 0.965. We could thus identify that a proposed method is able to apply to change detection between different network data-sets.

• Journal of the Korean Geophysical Society
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• v.3 no.3
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• pp.175-184
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• 2000

A nondestructive experimental feasibility study was conducted using magnetometer to find buried cultural objects at pottery and steel matters in low-relief mountaineous area of Doojeong-dong, Cheonan, Chungnam Province from May 23 to July 18, 1998. Magnetic survey was carried out with $20cm{\times}20cm$ grid in a site of $20m{\times}40m$ before excavation, and the distribution of magnetic anomalies was compared with the results of excavation. Magnetic sensor was located on the surface of ground during the magnetic survey on the basis of an experimental result. Positive magnetic anomalies of maximum 130 nT are found over a pair of potteries. Magnetic anomaly map reveals several anomalous points in the 1st and 4th quadrants of the survey site, from where potteries and their fragments were confirmed. Six points out of seven points cprrelated with magnetic anomaly are found contain earthwares, whereas a magnetically uncorrelated location produced earthware made of unbaked clay. Steel waste such as cans and wires hidden in soil and bushes also influenced magnetic anomalies. Therefore, it is better to remove such steel wastes prior to magnetic survey if possible. Some magnetically anomalous points produced no archaeological object on excavation. This may be explained by shallower level of excavation than burial depth.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.159-185
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• 2016

Planning a multi-day trip is a complex, yet time-consuming task. It usually starts with selecting a list of points of interest (POIs) worth visiting and then arranging them into an itinerary, taking into consideration various constraints and preferences. When choosing POIs to visit, one might ask friends to suggest them, search for information on the Web, or seek advice from travel agents; however, those options have their limitations. First, the knowledge of friends is limited to the places they have visited. Second, the tourism information on the internet may be vast, but at the same time, might cause one to invest a lot of time reading and filtering the information. Lastly, travel agents might be biased towards providers of certain travel products when suggesting itineraries. In recent years, many researchers have tried to deal with the huge amount of tourism information available on the internet. They explored the wisdom of the crowd through overwhelming images shared by people on social media sites. Furthermore, trip planning problems are usually formulated as 'Tourist Trip Design Problems', and are solved using various search algorithms with heuristics. Various recommendation systems with various techniques have been set up to cope with the overwhelming tourism information available on the internet. Prediction models of recommendation systems are typically built using a large dataset. However, sometimes such a dataset is not always available. For other models, especially those that require input from people, human computation has emerged as a powerful and inexpensive approach. This study proposes CYTRIP (Crowdsource Your TRIP), a multi-day trip itinerary planning system that draws on the collective intelligence of contributors in recommending POIs. In order to enable the crowd to collaboratively recommend POIs to users, CYTRIP provides a shared workspace. In the shared workspace, the crowd can recommend as many POIs to as many requesters as they can, and they can also vote on the POIs recommended by other people when they find them interesting. In CYTRIP, anyone can make a contribution by recommending POIs to requesters based on requesters' specified preferences. CYTRIP takes input on the recommended POIs to build a multi-day trip itinerary taking into account the user's preferences, the various time constraints, and the locations. The input then becomes a multi-day trip planning problem that is formulated in Planning Domain Definition Language 3 (PDDL3). A sequence of actions formulated in a domain file is used to achieve the goals in the planning problem, which are the recommended POIs to be visited. The multi-day trip planning problem is a highly constrained problem. Sometimes, it is not feasible to visit all the recommended POIs with the limited resources available, such as the time the user can spend. In order to cope with an unachievable goal that can result in no solution for the other goals, CYTRIP selects a set of feasible POIs prior to the planning process. The planning problem is created for the selected POIs and fed into the planner. The solution returned by the planner is then parsed into a multi-day trip itinerary and displayed to the user on a map. The proposed system is implemented as a web-based application built using PHP on a CodeIgniter Web Framework. In order to evaluate the proposed system, an online experiment was conducted. From the online experiment, results show that with the help of the contributors, CYTRIP can plan and generate a multi-day trip itinerary that is tailored to the users' preferences and bound by their constraints, such as location or time constraints. The contributors also find that CYTRIP is a useful tool for collecting POIs from the crowd and planning a multi-day trip.

• Korean Journal of Heritage: History & Science
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• v.56 no.4
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• pp.54-70
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• 2023

Surveys and studies on cultural assets using LiDAR measurement are already active overseas. Recently, awareness of the advantages and availability of LiDAR measurement has increased in Korea, and cases of using it for surveys of cultural assets are gradually increasing. However, it is usually restricted to surveys of mountain fortresses and is not actively used for surveys of ancient tombs yet. Therefore, this study intends to emphasize the need to secure fundamental data from LiDAR measurement for the era from the Three Kingdoms to Unified Silla in which recovery, maintenance, etc., in addition to the actual surveys, are unfulfilled due to the sites being mainly distributed in mountainous areas. For this, LiDAR measurement was executed for the area of Jangsan Ancient Tombs and Chunghyo-dong Ancient Tombs in Seoak-dong, Gyeongju, to review the distribution and geographical conditions of ancient tombs. As a result, in the Jangsan Ancient Tombs, in which a precision archaeological (measurement) survey was already executed, detailed geographic information and distribution conditions could be additionally identified, which could not be known only with the layout indicated by the topographic map of the existing report. Also, in the Chunghyo-dong Ancient Tombs, in which an additional survey was not conducted after 10 tombs were found during the Japanese colonial period, the location of the ancient tombs initially excavated was accurately identified, and the status and additional information was acquired, such as on the conditions of ancient tombs not surveyed. Such information may also be used as fundamental data for the preservation and maintenance of future ancient tombs in addition to the survey and study of the ancient tombs themselves. LiDAR measurement is most effective for identifying the condition of ancient tombs in mountainous areas where observation is difficult or access is limited due to the forest zone. It may be executed before on-site surveys, such as archaeological surveys, to secure data with high availability as prior surveys or pre-surveys. Therefore, it is necessary to secure fundamental data from LiDAR measurement in future surveys of ancient tombs and to establish a survey and maintenance/utilization plan based on this. To establish survey/study and preservation/maintenance measures for ancient tombs located in mountainous areas, a precision archaeological survey is currently executed to draw up a distribution chart of ancient tombs. If LiDAR measurement data is secured before this and used, a more effective and accurate distribution chart can be drawn up, and the actual conditions can be identified. Also, most omissions or errors in information can be prevented in on-site surveys of large regions. Therefore, it is necessary to accumulate fundamental data by actively using LiDAR measurement in future surveys of ancient tombs.