• Journal of KIISE:Software and Applications
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• v.31 no.11
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• pp.1431-1438
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• 2004

Reinforcement learning learns policies for accomplishing a task's goal by experience through interaction between agent and environment. Q-learning, basis algorithm of reinforcement learning, has the problem of curse of dimensionality and slow learning speed in the incipient stage of learning. In order to solve the problems of Q-learning, new function approximation methods suitable for reinforcement learning should be studied. In this paper, to improve these problems, we suggest Fuzzy Q-Map algorithm that is based on online fuzzy clustering. Fuzzy Q-Map is a function approximation method suitable to reinforcement learning that can do on-line teaming and express uncertainty of environment. We made an experiment on the mountain car problem with fuzzy Q-Map, and its results show that learning speed is accelerated in the incipient stage of learning.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.735-739
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• 2005

This paper presents a new system to estimate the head pose of human in interactive indoor environment that has dynamic illumination change and large working space. The main idea of this system is to suggest a new morphological feature for estimating head angle from stereo disparity map. When a disparity map is obtained from stereo camera, the matching confidence value can be derived by measurements of correlation of the stereo images. Applying a threshold to the confidence value, we also obtain the specific morphology of the disparity map. Therefore, we can obtain the morphological shape of disparity map. Through the analysis of this morphological property, the head pose can be estimated. It is simple and fast algorithm in comparison with other algorithm which apply facial template, 2D, 3D models and optical flow method. Our system can automatically segment and estimate head pose in a wide range of head motion without manual initialization like other optical flow system. As the result of experiments, we obtained the reliable head orientation data under the real-time performance.

• Journal of Korea Spatial Information System Society
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• v.11 no.3
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• pp.47-49
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• 2009

In this paper, we proposed a new algorithm for determination of many-to-many corresponding pairs between block-polygon sets from the national topographical map and the cadastral map in Korea Land Information System, caused by different abstraction and generalization rules of the two maps. Our proposed algorithm starts from an assumption that a block-polygon for a N:M pair should significantly overlap at least one block polygon of the counterpart group, and determines N:M pairs using an iterative updating and searching with this overlapping analysis. This iteration process is terminated when the N:M corresponding pairs satisfy our predefined 1:1 corresponding condition.

• The Transactions of the Korea Information Processing Society
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• v.1 no.3
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• pp.391-397
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• 1994

In this paper, we have developed a PLD Designer which is a design tool for digital circuits design using PLD device. PLD designer consists of a state graphic editor to extract boolean equations from state table within 20 states of FSM and a pin map editor to assign pin map for PLD device(PAL16R4, PAL22V10, GAL16V8, etc), which is suitable for extracted boolean equations. Also pin map editor generates a necessary JEDEC file to implement PLD device by using fuse map and checksum algorithm. To verify extracted boolean equation, we have developed simulation test vector generation algorithm. The results of JEDEC files generated by PLD designer is same with the results of JEDEC files generated by PALASM.

• Journal of Auto-vehicle Safety Association
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• v.11 no.2
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• pp.29-34
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• 2019

This paper presents a new method for construction of a static obstacle map. A static obstacle is important since it is utilized to path planning and decision. Several established approaches generate static obstacle map by grid method and counting algorithm. However, these approaches are occasionally ineffective since the density of LiDAR layer is low. Our approach solved this problem by applying probability theory. First, we converted all LiDAR point to Gaussian distribution to considers an uncertainty of LiDAR point. This Gaussian distribution represents likelihood of obstacle. Second, we modeled dynamic transition of a static obstacle map by adopting the Hidden Markov Model. Due to the dynamic characteristics of the vehicle in relation to the conditions of the next stage only, a more accurate map of the obstacles can be obtained using the Hidden Markov Model. Experimental data obtained from test driving demonstrates that our approach is suitable for mapping static obstacles. In addition, this result shows that our algorithm has an advantage in estimating not only static obstacles but also dynamic characteristics of moving target such as driving vehicles.

• ETRI Journal
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• v.44 no.2
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• pp.183-193
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• 2022

Smart cities are expected to provide residents with convenience via various agents such as CCTV, delivery robots, security robots, and unmanned shuttles. Environmental data collected by various agents can be used for various purposes, including advertising and security monitoring. This study suggests a surveillance map data framework for efficient and integrated multimodal data representation from multi-agents. The suggested surveillance map is a multilayered global information grid, which is integrated from the multimodal data of each agent. To confirm this, we collected surveillance map data for 4 months, and the behavior patterns of humans and vehicles, distribution changes of elevation, and temperature were analyzed. Moreover, we represent an anomaly detection algorithm based on a surveillance map for security service. A two-stage anomaly detection algorithm for unusual situations was developed. With this, abnormal situations such as unusual crowds and pedestrians, vehicle movement, unusual objects, and temperature change were detected. Because the surveillance map enables efficient and integrated processing of large multimodal data from a multi-agent, the suggested data framework can be used for various applications in the smart city.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.123-123
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• 2020

The present study is aimed to correcting radar-based mean areal precipitation forecasts to improve urban flood predictions and uncertainty analysis of water levels contributed at each stage in the process. For this reason, a long short-term memory (LSTM) network is used to reproduce three-hour mean areal precipitation (MAP) forecasts from the quantitative precipitation forecasts (QPFs) of the McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation (MAPLE). The Gangnam urban catchment located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 24 heavy rainfall events, 22 grid points from the MAPLE system and the observed MAP values estimated from five ground rain gauges of KMA Automatic Weather System. The corrected MAP forecasts were input into the developed coupled 1D/2D model to predict water levels and relevant inundation areas. The results indicate the viability of the proposed framework for generating three-hour MAP forecasts and urban flooding predictions. For the analysis uncertainty contributions of the source related to the process, the Bayesian Markov Chain Monte Carlo (MCMC) using delayed rejection and adaptive metropolis algorithm is applied. For this purpose, the uncertainty contributions of the stages such as QPE input, QPF MAP source LSTM-corrected source, and MAP input and the coupled model is discussed.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.299-309
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• 2001

In PET(Positron Emission Tomography), it is necessary to use transmission scan data in order to estimate the attenuation map. Recently, there are several empirical studies in which one might be able to estimate attenuation map and activity distribution simultaneously with emissive sinogram alone without transmission scan. However, their algorithms are based on the model in which does not include the background counts term, and so is unrealistic. If the background counts component has been included in the model, their algorithm would introduce non-monotonic reconstruction algorithm which results in vain in practice. in this paper, we develop a monotonic and parallelizable algorithm for simultaneous reconstruction of both characteristics and present the validity through some simulations.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11b
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• pp.899-902
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• 2003

무선 환경에서의 실시간 서비스의 확대와 노드의 이동성 증가에 따라 handover 시 발생되는 패킷 손실과 지연으로 인한 실시간 서비스의 중단이 해결해야 할 중요한 문제점이 되고 있다. 본 논문에서는 IETF (Internet Engineering Task Force)의 중요한 마이크로 이동성 프로토콜인 HMIPv6의 기본 동작을 설명하고, 여전히 문제점으로 남아있는 Inter-MAP (Mobility Anchor Point) handover의 성능을 향상시키기 위한 Dynamic MAP Switching handover 알고리듬을 제안한다. 제안한 알고리듬은 인접한 MAP 도메인을 중첩되게 설정하고 Inter-MAP handover가 발생되기 전에 미리 MAP을 switching한다. 따라서 Inter-MAP handover가 발생하지 않도록 함으로써 handover 시 발생하는 데이터의 손실 및 지연을 감소시킬 수 있다. 제안된 알고리듬의 성능 향상은 산술적 분석을 통하여 검증한다.

• Journal of Digital Convergence
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• v.10 no.8
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• pp.211-217
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• 2012

Conventional GrabCut algorithm is semi-automatic algorithm that user must be set rectangle window surrounds the object. This paper studied automatic object detection to solve these problem by detecting salient region based on Human Visual System. Saliency map is computed using Lab color space which is based on color opposing theory of 'red-green' and 'blue-yellow'. Then Saliency Points are computed from the boundaries of Low-Frequency region that are extracted from Saliency Map. Finally, Rectangle windows are obtained from coordinate value of Saliency Points and these windows are used in GrabCut algorithm to extract objects. Through various experiments, the proposed algorithm computing rectangle windows of salient region and extracting objects has been proved.