• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.65-68
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• 2007

DOCSIS 3.0 MAC protocol regulate CM and CMTS Channel transfer times through MAP message. Standards does not include the details of MAP Inter-arrival Time affecting the performance of MAC protocols for DOCSIS 3.0. In this paper, we evaluationed the performance of protocol follow in MAP Inter-arrival Time on DOCSIS 3.0. Based on the evaluation results, we propose the optimal MAP Inter-arrival Time. We found that the protocol shows best performance when the MAP Inter-arrival Time is 0.05sec. The research results can apply to performance element which important for the construction of DOCSIS 3.0 base cable networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1069-1076
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• 2007

DOCSIS 3.0 protocol are proposed to support high-speed cable network DOCSIS 3.0 MAC protocol regulates CM and CMTS channel transfer times through MAP message. So MAP Inter-arrival time is primary factor of network performance. However, standards does not include the details of MAP Inter-arrival Time affecting the performance of MAC protocols for DOCSIS 3.0. In this paper, we evaluated the performance of DOCSIS 3.0 protocol follow in MAP Inter-arrival Time. Based on the evaluation results, we propose the optimal MAP Inter-arrival Time. We found that the protocol shows best performance when the MAP Inter-arrival Time is 0.05sec. The research results can apply to performance element which important for the construction of DOCSIS 3.0 base cable networks.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1056-1059
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• 2003

Self-Oranizing Map(SOM) is an unsupervised neural network providing cluster analysis of high dimensional input data. The output from the SOM is represented in map that help us to explore data. The weak point of conventional SOM is when the map is large, it take a long time to train the data. The computing time is known to be O(MN) for trainning to find the winning node (M,N are the number of nodes in width and height of the map). This paper presents a new method to reduce the computing time by creating new map. Each node in a new map is the centroid of nodes' group that are in the original map. After create a new map, we find the winning node of this map, then find the winning node in original map only in nodes that are represented by the winning node from the new map. This new method is called "High Speed Self-Oranizing Map"(HS-SOM). Our experiment use HS-SOM to cluster documents and compare with SOM. The results from the experiment shows that HS-SOM can reduce computing time by 30%-50% over conventional SOM.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.382-388
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• 1987

In this paper, the performance of Mini MAP architecture is analyzed by simulation method. Token rotation time and waiting time are obtained by simulation. The results of the simulation are compared with these of the analytic model. From these comparisons, it is shown that simulation results are approximately identical to analytic results. Mini MAP architecture has good real time performances in token rotation time and waiting time and can be used to many real time applications.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1127-1130
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• 2003

This paper introduces histogramic in-motion mapping for real-time map building with the Eyebot in motion. A histogram grid used in HIMM is updated through three PSD sensors. HIMM makes it possible to make fast map-building and avoid obstacles in real-time. Fast map-building allows the robot to immediately use the mapped information in real-time obstacle-avoidance algorithms. HIMM has been tested on the Eyebot. The Eyebot sends PSD data to computer and computer builds a 3D-Map based on PSD data.

• Journal of Cadastre & Land InformatiX
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• v.47 no.2
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• pp.49-60
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• 2017

Recently, the use of mashup that integrates real-time data with spatial data such as tiled map and satellite imagery has been increased significantly. As the use of mashup has been extended to various fields of O2O, LBS, Smart City, and Autonomous Driving, the performance of mashup has become more important. Therefore, this study aims to compare and analyze the performance of various map platforms, when large amounts of real-time data are integrated with spatial data. Specifically, we compare the performance of most popular map platforms available in Korea, such as Google Maps, OpenStreetMap, Daum Map, Naver Map, olleh Map, and VWorld. We also compare the performance using most common web browsers of Chrome, Firefox and Internet Explorer. In the performance analysis, we measured and compared the initialization time of basic map and the mashup time of real-time data for the above map platforms. From analysis results, we could find that Google Maps, OpenStreetMap, VWorld, and olleh Map platforms showed a better performance than the others.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.1
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• pp.23-32
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• 2017

In this paper, We suggest a unified approach for the analysis of discrete-time MAP/G/1 queueing system. Many researches on the D-MAP/G/1 queue have been used different approach to analyze system queue length and waiting time for the same system. Therefore, a unified framework for analyzing a system is necessary from a viewpoint of system design and management. We first derived steady-state workload distribution, and then waiting time and sojourn time are derived by the result of workload analysis. Finally, system queue length distribution is derived with generating function from the sojourn time distribution.

• Journal of the Korea Society of Computer and Information
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• v.24 no.4
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• pp.51-56
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• 2019

In this paper, we propose an algorithm that can quickly generate and synthesize city maps in racing games. Racing games are characterized by moving a wide map rather than a fixed map, but designing and developing a wide map requires a lot of production time. This problem can be mitigated by creating a large map in the preprocessing step, but a fixed map makes the game tedious. It is also inefficient to process all the various maps in the preprocessing step. In order to solve this problem, we propose a technique to create a terrain pattern in the preprocessing process, to generate a map in real time, and to synthesize various maps randomly. In addition, we reduced unnecessary rendering computations by integrating view-dependent techniques into the proposed framework. This study was developed in Unity3D and can be used for various contents as well as racing game.

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

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.536-538
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• 2006

The most important factor of mobile robot is to build a map for surrounding environment and estimate its localization. This paper proposes a real-time localization and map building method through 3-D reconstruction using scale invariant feature from monocular camera. Mobile robot attached monocular camera looking wall extracts scale invariant features in each image using SIFT(Scale Invariant Feature Transform) as it follows wall. Matching is carried out by the extracted features and matching feature map that is transformed into absolute coordinates using 3-D reconstruction of point and geometrical analysis of surrounding environment build, and store it map database. After finished feature map building, the robot finds some points matched with previous feature map and find its pose by affine parameter in real time. Position error of the proposed method was maximum. 8cm and angle error was within $10^{\circ}$.