• Journal of Information Processing Systems
• /
• v.14 no.4
• /
• pp.877-891
• /
• 2018

In this paper, we propose a novel algorithm for rendering motion-blurred shadows utilizing a depth-time ranges shadow map. First, we render a scene from a light source to generate a shadow map. For each pixel in the shadow map, we store a list of depth-time ranges. Each range has two points defining a period where a particular geometry was visible to the light source and two distances from the light. Next, we render the scene from the camera to perform shadow tests. With the depths and times of each range, we can easily sample the shadow map at a particular receiver and time. Our algorithm runs entirely on GPUs and solves various problems encountered by previous approaches.

• Journal of Korea Multimedia Society
• /
• v.18 no.3
• /
• pp.298-311
• /
• 2015

Vector data represents a map by its coordinate and Raster data represents a map by its pixel. Since these data types have very large data size, data compression procedure is a compulsory process. This paper compare the results from three different methodologies; GIS (Geographic Information System) vector map data compression using DP(Douglas-Peucker) Simplification algorithm, vector data compression based on Bin classification and the combination between two previous methods. The results shows that the combination between the two methods have the best performance among the three tested methods. The proposed method can achieve 4-9% compression ratio while the other methods show a lower performance.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.16 no.3
• /
• pp.107-111
• /
• 2021

A 3D point cloud map is an essential elements in various fields, including precise autonomous navigation system. However, generating a 3D point cloud map using a single sensor has limitations due to the price of expensive sensor. In order to solve this problem, we propose a precise 3D mapping system using low-cost sensor fusion. Generating a point cloud map requires the process of estimating the current position and attitude, and describing the surrounding environment. In this paper, we utilized a commercial visual-inertial odometry sensor to estimate the current position and attitude states. Based on the state value, the 2D LiDAR measurement values describe the surrounding environment to create a point cloud map. To analyze the performance of the proposed algorithm, we compared the performance of the proposed algorithm and the 3D LiDAR-based SLAM (simultaneous localization and mapping) algorithm. As a result, it was confirmed that a precise 3D point cloud map can be generated with the low-cost sensor fusion system proposed in this paper.

• Journal of KIISE:Information Networking
• /
• v.33 no.6
• /
• pp.451-460
• /
• 2006

In HMIPv6, the nested mobile network because of the mobility of node and router can be constituted. Many subnets exist and many mobile router(MR)s and mobile node(MN)s activate in the nested mobile network. If the nested depth is deeper, the number of mobile router that packet goes through, increases and data transmission delay owing to this might be larger. This paper proposes EPA algorithm which finds out the path from Mobility Anchor Point(MAP) to a destination mobile node using the binding cache information of MAP and processes the path information by adding it to packet header. If we apply EAP algorithm, the quantity of unnecessary packet within MAP domain can be decreased. Also, the transmission delay can be decreased in a intermediate mobile router because it supports packet re-transmission just by simple packet substitution.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.219-221
• /
• 2022

In recent summer, as it is concentrated, even in mountainous areas, flooding and flooding cause casualties in pedestrian evacuation situations. To compensate for this, a system that detects the occurrence of flooding and allows pedestrians to evacuate safely is required. Therefore, in this paper, we propose a research on pedestrian path search based on the shortest distance algorithm using Map API. The pedestrian route search system outputs a map using the T Map API, selects nearby buildings as shelters, and stores data. A shelter close to the pedestrian's current location is selected, and the shortest route is output and the distance and time are provided. If there is a problem with the current route during evacuation, another shelter route is provided from the current location. Therefore, it is thought that the pedestrian route search evacuation system proposed in this paper will prevent accidents during evacuation.

• Proceedings of the IEEK Conference
• /
• 2003.11a
• /
• pp.297-300
• /
• 2003

In this paper, fingerprint minutiae mosaicking algorithm using minutiae of fingerprint is proposed. First, minutiae map is generated from minutiae of fingerprint and minutiae constellation is generated from fingerprint minutiae map. Minutiae constellation is constellation-shaped structure generated from Voronoi Diagram and Delaunay Triangulation using information of minutiae. Secondly, common region is detected by similarity of minutiae constellation of fingerprint minutiae map and minutiae map of individual fingerprint image is composed. Consequently composite minutiae map by mosaicking of fingerprint minutiae improve the performance of the fingerprint matching system.

• Proceedings of the Acoustical Society of Korea Conference
• /
• 1994.06a
• /
• pp.1046-1051
• /
• 1994

It is known that SOFM has the property of effectively creating topographically the organized map of various features on input signals, SOFM can effectively be applied to the recognition of Korean phonemes. However, is isn't guaranteed that the network is sufficiently learned in SOFM algorithm. In order to solve this problem, we propose the learning algorithm combined with the conventional K-means clustering algorithm in fine-tuning stage. To evaluate the proposed algorithm, we performed speaker dependent recognition experiment using six phoneme classes. Comparing the performances of the Kohonen's algorithm with a proposed algorithm, we prove that the proposed algorithm is better than the conventional SOFM algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.19 no.2
• /
• pp.199-208
• /
• 2001

The digital map in Korea has been producted and utilized independently according to scales such as 1:1,000, 1:5,000, and 1:25,000. Therefore, whenever we need to obtain the spatial data of other scales, we have to product the digital maps over and over again which it is time-consuming and ineconomic. To solve these problems, it has been accomplished many researches on map generalization to make digital maps in small scale from the master data of large scale. This paper aims to analyze the conversion characteristics of the large scale to the small scale by simplification of map generalization. For this purpose, it is proposed the algorithm for the simplification process of digital map and it is investigated the simplification characteristic of digital map through the experiment on the conversion of 1:5,000 scale into 1:25.000 scale. The results show that Area-Preservation algorithm indicates the good agreement with the original data in terms of the area and features of building layer compared to Douglas-Peucker algorithm and Reumann-Witkam algorithm.

• Journal of Information Technology and Architecture
• /
• v.9 no.4
• /
• pp.455-464
• /
• 2012

Due to the recent data explosion, methods which can meet the requirement of large data analysis has been studying. This paper proposes MRIterativeBUC algorithm which enables efficient computation of large data cube by distributed parallel processing with MapReduce framework. MRIterativeBUC algorithm is developed for efficient iterative operation of the BUC method with MapReduce, and overcomes the limitations about the storage size and processing ability caused by large data cube computation. It employs the idea from the iceberg cube which computes only the interesting aspect of analysts and the distributed parallel process of cube computation by partitioning and sorting. Thus, it reduces data emission so that it can reduce network overload, processing amount on each node, and eventually the cube computation cost. The bottom-up cube computation and iterative algorithm using MapReduce, proposed in this paper, can be expanded in various way, and will make full use of many applications.

• ETRI Journal
• /
• v.32 no.6
• /
• pp.891-900
• /
• 2010

In this paper, a map-matching (MM) algorithm which combines an estimated position with digital road data is proposed. The presented algorithm using a virtual track is appropriate for a MEMS-based pedestrian dead reckoning (PDR) system, which can be used in mobile devices. Most of the previous MM algorithms are for car navigation systems and GPS-based navigation system, so existing MM algorithms are not appropriate for the pure DR-based pedestrian navigation system. The biggest problem of previous MM algorithms is that they cannot determine the correct road segment (link) due to the DR characteristics. In DR-based navigation system, the current position is propagated from the previous estimated position. This means that the MM result can be placed on a wrong link when MM algorithm fails to decide the correct link at once. It is a critical problem. Previous algorithms never overcome this problem because they did not consider pure DR characteristics. The MM algorithm using the virtual track is proposed to overcome this problem with improved accuracy. Performance of the proposed MM algorithm was verified by experiments.