• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.471-478
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• 2020

In this paper, we develop the ground risk model of unmanned aerial vehicle (UAV) operation to quantify the ground risk when the UAV falls to the ground during the intended operation in case of UAV failure. The ground risk is computed by using the UAV failure probability, the probability of impact a person when UAV falls to the ground, the probability of fatality when UAV strikes the person. We mathematically derive each probability to evaluate the ground risk of UAV operation. Also, the population density map, building to land ratio map, car traffic database is used to estimate the number of people exposed to collision with UAV. Finally, we assumed the operations of a UAV with two paths in Daejeon city and evaluate the ground risk of each UAV operations.

• Journal of the Korean GEO-environmental Society
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• v.22 no.11
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• pp.5-13
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• 2021

The interest of expecting the liquefaction damage is increasing due to the liquefaction in Pohang in 2017. Liquefaction is defined as a phenomenon that the ground can not support the superstructure due to loss of the strength of the ground. As an alternative against this, many studies are being conducted to increase the precision and to compose a liquefaction hazard map for the purpose of identifying the scale of liquefaction damage using the liquefaction potential index (LPI). In this research, in order to analyze the degree of precision with regard to spatial interpolation objects such as LPI value and geotechnical information for LPI determination, liquefaction hazard map were made for the target area. Furthermore, based on the trend of precision, probability value was analyzed using probability maps prepared through qualitative characteristics. Based on the analysis results, the precision of the liquefaction hazard map setting the spatial interpolation object as geotechnical information is higher than that as LPI value. Furthermore, the precision of the liquefaction hazard map does not affect the distribution of the probability value.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2451-2453
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• 2002

In this paper, we present an infrared sensors aided map building method for mobile robot using genetic algorithm and fuzzy logic. Existing Bayesian update model using ultrasonic sensors only has a problem of the quality of map being degraded in the wall with irregularity which is caused by the wide beam width of sonar waves and Gaussian probability distribution. In order to solve this problem we propose an improved method of map building using supplementary infrared sensors. In the method, wide beam width of sonar waves is divided by infrared sensors and probability is distributed according to infrared sensors' information using fuzzy logic and genetic algorithm.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.6
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• pp.77-87
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• 2019

The first grade zones in Ecology and Nature Map are important regions for the conservation of the ecosystem, but it would be degraded by various anthropogenic factors. This study analyzes the relationship between potential land cover change and degradation of the first grade zones using land cover transition probability. As a result, it was shown that most of the first grade zones with degraded were converted from forest to urban(5.1%), cropland(27.2%), barren(11.0%) and grass(27.5%) in Gangwon and forest to urban(18.0%), cropland(15.3%), grass(28.4%), barren(12.3%) in Gyeonggi. The result of the logistic regression analysis showed that the probability of degradation of first grade zone was higher in area where was expected the higher probability of urban, cropland, barren, grass transition. The barren transition probability was the most influential and grass was the next highest. There were regional differences in the probability of urban transition and cropland transition, and the urban transition probability was more influential in Gyeonggi-do. This is because development pressure such as housing site development is high in Gyeonggi-do. Due to the limitations of the Act on Mountain Districts Management, even in the first grade zones, the grade may be degraded. Therefore, if Ecology and Nature Map are used to prevent deforestation or conversion of mountainous districts, it may contribute to the preservation of the ecosystem.

• 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.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.4
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• pp.675-683
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• 1992

Mapping and navigation system based on certainty grids for an autonomous mobile robt operating in unknown and unstructured environment is described. The system uses sonar range data to build a map of robot's surroundings. The range data from sonar sensor are integrated into a probability map that is composed of two dimensional grids which contain the probabilities of being occupied by the objects in the environment. A Bayesian model is used to estimate the uncertainty of the sensor information and to update the existing probability map with new range data. The resulting two dimensional map is used for path planning and navigation. In this paper, the Bayesian updating model which was successfully simulated in our earlier work is implemented on a mobile robot and is shown to be valid in the real world through experiment. This paper also proposes a technique for reducing for reducing specular reflection problem of sonar system which seriousely deteriorates the map quality, and a new path planning method based on weighted distance, which enables the robot to efficiently navigate in an unknown area.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.8
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• pp.3028-3035
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• 2010

This paper propose a genetic method based on probability map to detect region of the lips on a natural image with the faces. The method has many solutions in order to detect regions such as the lips instead of one optimal solution of existing methods. To do this, it represents a pair of spatial coordinates as a chromosome, and introduces genetic operations like conservation interval, the number of generations and non-overlapping selection. By using the probability map of the HS in HSV color space, it increases adaptability to similar color that is a property of genetic algorithm. In our experiments, the optimal value of the important parameter $\beta$ was analyzed, which was used as the condition of an ending function and affected performance of the proposed algorithm. Also the algorithm was analyzed on what performance it has when its mating methods are different. The results of the experiment showed that our algorithm could be flexibly adapted for detecting other ROIs.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.378-381
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• 2008

The regulating and relaxing targets in the Land Use Regulation and Total Maximum Daily Loads are influenced by Land cover information. For the providing more accurate land information, this study attempted to generate an impervious surface map using KOMPSAT-2 image which a Korea manufactured high resolution satellite image. The classification progress of this study carried out by tasseled-cap spectral enhancement through each class extraction technique neither existing classification method. KOMPSAT-2 image of this study is enhanced by Soil Brightness Index(SBI), Green vegetation Index(GVI), None-Such wetness Index(NWI). Then ranges of extracted each index in enhanced image are determined. And then, Confidence Interval of classes was determined through the calculating Non-exceedance Probability. Spectral distributions of each class are changed according to changing of Control coefficient(${\alpha}$) at the calculated Non-exceedance Probability. Previously, Land cover classification map was generated based on established ranges of classes, and then, pervious and impervious surface was reclassified. Finally, impervious ratio of reclassified impervious surface map was calculated with blocks in the study area.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.12C
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• pp.738-743
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• 2011

For the high quality 3-D broadcasting, depth maps are important data. Although commercially available depth cameras capture high-accuracy depth maps in real time, their resolutions are much smaller than those of the corresponding color images due to technical limitations. In this paper, we propose the depth map up-sampling method using a high-resolution color image and a low-resolution depth map. We define a random walk probability model in an operation unit which has nearest seed pixels. The proposed method is appropriate to match boundaries between the color image and the depth map. Experimental results show that our method enhances the depth map resolution successfully.

• ETRI Journal
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• v.29 no.2
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• pp.243-245
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• 2007

This letter presents a new feature extraction method based on the private energy map (PEM) technique to utilize the energy characteristics of a facial image. Compared with a non-facial image, a facial image shows large energy congestion in special regions of discrete cosine transform (DCT) coefficients. The PEM is generated by energy probability of the DCT coefficients of facial images. In experiments, higher face recognition performance figures of 100% for the ORL database and 98.8% for the ETRI database have been achieved.