• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.693-699
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• 2006

An accurate and compact map is essential to an autonomous mobile robot system. For navigation, it is efficient to use an occupancy grid map because the environment is represented by probability distribution. But it is difficult to apply it to the large environment since it needs a large amount of memory proportional to the environment size. As an alternative, a topological map can be used to represent it in terms of the discrete nodes with edges connecting them. It is usually constructed by the Voronoi-like graphs, but in this paper the topological map is incrementally built based on the local grid map using the thinning algorithm. This algorithm can extract only meaningful topological information by using the C-obstacle concept in real-time and is robust to the environment change, because its underlying local grid map is constructed based on the Bayesian update formula. In this paper, the position probability is defined to evaluate the quantitative reliability of the end nodes of this thinning-based topological map (TTM). The global TTM can be constructed by merging each local TTM by matching the reliable end nodes determined by the position probability. It is shown that the proposed TTM can represent the environment accurately in real-time and it is readily extended to the global TTM.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.4
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• pp.575-582
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• 2013

This paper proposes a map building using the ICP algorithm based robot localization prediction. Proposed method predicts a robot location to dead reckoning, makes a map in the ICP algorithm. Existing method makes a map building and robot position using a sensor value of reference data and current data. In this case, a large interval of the difference of the reference data and the current data is difficult to compensate. The proposed method can map correction through practical experiments.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.609-619
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• 2016

RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. In this paper, we implement K-Means algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. The main idea introduces a combiner as a function of our map output to decrease the amount of data needed to be processed by reducers. We showed that our K-Means algorithm using RHadoop with combiner was faster than regular algorithm without combiner as the size of data set increases. We also implemented Elbow method with MapReduce for finding the optimum number of clusters for K-Means clustering on large dataset. Comparison with our MapReduce implementation of Elbow method and classical kmeans() in R with small data showed similar results.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.330-336
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• 2005

Existing Bayesian update method using ultrasonic sensors only for mobile robot map building has a problem of the quality of map being degraded in the wall with irregularity, which is caused by the wide beam distribution. For improving this problem, an infrared sensors aided map building method is presented in this paper. Information of obstacle at each region in ultrasonic sensor beam is acquired using the infrared sensors and the information is used to get the confidence of ultrasonic sensor information via fuzzy inference system and genetic algorithm. Combining the resulting confidence with the result of Bayesian update method, an improve map is constructed. The proposed method showed good results in the simulations and experiments.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.2
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• pp.213-218
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• 2013

In this paper, a video based ship detection method is proposed to monitor port efficiently. Visual saliency map algorithm and mean shift algorithm is applied to detect moving ships don't include background information which is difficult to track moving ships. It is easy to detect ships at the port using saliency map algorithm, because it is very effective to extract saliency object from background. To remove background information in the saliency region, image segmentation and clustering using mean shift algorithm is used. As results of detecting simulation with images of a camera installed at the harbor, it is shown that the proposed method is effective to detect ships.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11C
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• pp.1045-1051
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• 2003

The SOVA, which produces the soft decision value, can be used as a sub-optimum solution for concatenated codes such as turbo codes, since it is computationally efficient compared with the optimum MAP algorithm. In this paper, we propose an efficient implementation of the SOVA used for decoding turbo codes, by reducing the number of calculations for soft decision values and trace-back operations. In order to utilize the memory efficiently, the whole block of turbo codes is divided into several sub-blocks in the proposed algorithm. It is demonstrated that the proposed algorithm requires less computation than the conventional algorithm, while providing the same overall performance.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.3
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• pp.327-334
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• 2011

It is necessary that UGV(Unmanned Ground Vehicle) should generate a real-time travesability index map by analyzing raw terrain information to travel autonomously tough terrain which has various slope and roughness values. In this paper, we propose a local path planning method, $MA^*$(Modified $A^*$) algorithm, using DVGM (Directional Velocity Grid Map) for unmanned ground vehicle. We also present a path optimization algorithm and a path smoothing algorithm which regenerate a pre-planned local path by $MA^*$ algorithm into the reasonable local path considering the mobility of UGV. Field test is conducted with UGV in order to verify the performance of local path planning method using DVGM. The local path planned by $MA^*$ is compared with the result of $A^*$ to verify the safety and optimality of proposed algorithm.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.1
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• pp.54-62
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• 1999

Vectorizing for input processing of map is the most time and cost consuming task, and the quality of vector data depends on that processing result. Therefore, it is an important task to develop a good vectorizing system in the GIS. Thinning algorithm is the most important technology for deciding the quality of vector data in the vectorizing system. In this paper, as a suitable algorithm for map vectorizing we considered several algorithms that preserve topological and geometric characteristics, and have no distortion of the contour line. As a results, we implemented WPTA4 and well known thinning algorithm, and compared WPTA4 execution results with the others.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.2
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• pp.94-103
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• 2019

Self Organizing Map (SOM) is a neural network that is effective in classifying patterns that form the feature map by extracting characteristics of the input data. In this study, we propose an algorithm to determine the cell formation and the machine layout within the cell for the cell formation problem with operation sequence using the SOM. In the proposed algorithm, the output layer of the SOM is a one-dimensional structure, and the SOM is applied to the parts and the machine in two steps. The initial cell is formed when the formed clusters is grouped largely by the utilization of the machine within the cell. At this stage, machine cell are formed. The next step is to create a flow matrix of the all machine that calculates the frequency of consecutive forward movement for the machine. The machine layout order in each machine cell is determined based on this flow matrix so that the machine operation sequence is most reflected. The final step is to optimize the overall machine and parts to increase machine layout efficiency. As a result, the final cell is formed and the machine layout within the cell is determined. The proposed algorithm was tested on well-known cell formation problems with operation sequence shown in previous papers. The proposed algorithm has better performance than the other algorithms.

• 한국정보디스플레이학회:학술대회논문집
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• 2004.08a
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• pp.487-490
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• 2004

In order to simulate OLED evaporation process, modeling of directional distribution of the vaporized organic materials, film thickness distribution profile and pattern-mask shadow effect are required In accordance with many literatures; all of them except shadow effect modeling are studied and developed. In this paper, modeling algorithm of evaporation shadow is presented for process simulation of full-color OLED evaporating system. In OLED evaporating process the offset position of the point cell-source against the substrate rotation axis and the usage of the patterned mask are the principal causes for evaporation shadow. For geometric simulation of shadow using z-map, the film thickness profile, which is condensed on a glass substrate, is converted to the z-map data. In practical evaporation process, the glass substrate is rotated. This physical fact is solved and modeled mathematically for z-map simulation. After simulating the evaporation process, the z-map data can present the shadow-effected film thickness profile. Z-map is an efficient method in that the cross-sectional presentations of the film thickness profile and thickness distribution evaluation are easily and rapidly achieved.