• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.445-454
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• 2014

In recent days, trend technologies for cloud, bigdata, or mobile, as the important marketable keywords or paradigm in Information Communication Technology (ICT), are widely used and interrelated each other in the various types of platforms and web-based services. Especially, the combination of cloud and mobile is recognized as one of a profitable business models, holding benefits of their own. Despite these challenging aspects, there are a few application cases of this model dealing with geo-based data sets or imageries. Among many considering points for geo-based cloud application on mobile, this study focused on a performance test of mobile cloud of Bayesian image fusion algorithm with satellite images. Two kinds of cloud platform of Amazon and OpenStack were built for performance test by CPU time stamp. In fact, the scheme for performance test of mobile cloud is not established yet, so experiment conditions applied in this study are to check time stamp. As the result, it is revealed that performance in two platforms is almost same level. It is implied that open source mobile cloud services based on OpenStack are enough to apply further applications dealing with geo-based data sets.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.674-682
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• 2005

This paper proposes classification of multi-spectral satellite image based on fusion of fuzzy G-K (Gustafson-Kessel) algorithm and PCM algorithm. The suggested algorithm establishes the initial cluster centers by selecting training data from each category, and then executes the fuzzy G-K algorithm. PCM algorithm perform using classification result of the fuzzy G-K algorithm. The classification categories are allocated to the corresponding category when the results of classification by fuzzy G-K algorithm and PCM algorithm belong to the same category. If the classification result of two algorithms belongs to the different category, the pixels are allocated by Bayesian maximum likelihood algorithm. Bayesian maximum likelihood algorithm uses the data from the interior of the average intracluster distance. The information of the pixels within the average intracluster distance has a positive normal distribution. It improves classification result by giving a positive effect in Bayesian maximum likelihood algorithm. The proposed method is applied to IKONOS and Landsat TM remote sensing satellite image for the test. As a result, the overall accuracy showed a better outcome than individual Fuzzy G-K algorithm and PCM algorithm or the conventional maximum likelihood classification algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.4
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• pp.381-390
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• 2010

This paper describes a procedure of the map-based localization for mobile robots by using a sensor fusion technique in structured environments. A combination of various sensors with different characteristics and limited sensibility has advantages in view of complementariness and cooperation to obtain better information on the environment. In this paper, for robust self-localization of a mobile robot with a monocular camera and a laser structured light sensor, environment information acquired from two sensors is combined and fused by a Bayesian sensor fusion technique based on the probabilistic reliability function of each sensor predefined through experiments. For the self-localization using the monocular vision, the robot utilizes image features consisting of vertical edge lines from input camera images, and they are used as natural landmark points in self-localization process. However, in case of using the laser structured light sensor, it utilizes geometrical features composed of corners and planes as natural landmark shapes during this process, which are extracted from range data at a constant height from the navigation floor. Although only each feature group of them is sometimes useful to localize mobile robots, all features from the two sensors are simultaneously used and fused in term of information for reliable localization under various environment conditions. To verify the advantage of using multi-sensor fusion, a series of experiments are performed, and experimental results are discussed in detail.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.3
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• pp.175-181
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• 2020

For the success of a structural integrity management, it is essential to acquire structural response data at some critical locations with limited number of sensors. In this study, the structural response of numerical model was estimated by data fusion approach based on the Kalman filter known as stochastic recursive filter. Firstly, transient direct analysis was conducted to calculate the acceleration and strain of the numerical standing beam model, then the noise signals were mixed to generate the numerical measurement signals. The acceleration measurement signal was provided to the Kalman filter as an information on the external load, and the displacement measurement, which was transformed from the strain measurement by using strain-displacement conversion relationship, was provided into the Kalman filter as an observation information. Finally, the Kalman filter estimated the displacement by combining both displacements calculated from each numerically measured signal, then the estimated results were compared with the results of the transient direct analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3312-3334
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• 2015

In this paper, a mechanism for spectrum allocation in overlay cognitive radio networks is proposed. In overlay cognitive radio networks, the secondary users (SUs) must first sense the activity of primary users (PUs) to identify unoccupied spectrum bands. Based on their different contributions for the spectrum sensing, the SUs get payoffs that are computed by the fusion center (FC). The unoccupied bands will be auctioned and SUs are asked to bid using payoffs they earned or saved. Coalitions are allowed to form among SUs because each SU may only need a portion of the bands. We formulate the coalition forming process as a coalition forming game and analyze it by game theory. In the coalition formation game, debtor-creditor relationship may occur among the SUs because of their limited payoff storage. A debtor asks a creditor for payoff help, and in return provides the creditor with a portion of transmission time to relay data for the creditor. The negotiations between debtors and creditors can be modeled as a Bayesian game because they lack complete information of each other, and the equilibria of the game is investigated. Theoretical analysis and numerical results show that the proposed auction yields data rate improvement and certain fairness among all SUs.

• Journal of Korean Society of Transportation
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• v.17 no.3
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• pp.97-116
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• 1999

The growing demand for the real time traffic information is bringing about the category and number of traffic collection mechanism in the era of ITS. There are, however, two problems in making data into information using various traffic data. First, the information making process of making data into the representative information, for each traffic collection mechanism, for the specified analysis periods is required. Second, the integration process of fusing each representative information into "the information" for each link out of each source is also required. That is, both data reduction and/or data to information process and information fusion are required. This article is focusing on the development of information fusing algorithm based on voting technique, fuzzy regression, and, Bayesian pooling technique for estimating the dynamic link travel time of networks. The proposed algorithm has been validated using the field experiment data out of GPS probes and detectors over the roadways and the estimated link travel time from the algorithm is proved to be more useful than the mere arithmetic mean from each traffic source.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.2
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• pp.51-64
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• 2004

This paper proposes an algerian of generating a tuner-template from multiple fingerprint impressions using a data fusion technique for fingerprint enrollment. The super-template is considered as a single fingerprint template which contains most likely true minutiae based on multiple fingerprint images. The proposed algorithm creates the super template by utilizing a recursive Bayesian estimation method (RBEM), which assumes a sequential fingerprint input model and estimates the credibility of the minutiae in previous input templates froma current input template. Consequently. the RBEM assigns a higher credibility to commonly detectable minutiae from several input templates and a lower credibility to rarely found minutiae from other input templates. Likewise, the RBEM is able to estimate a credibility of the minutia type (ridge ending or bifurcation). Preliminary experiments demonstrate that, as the number of fingerfrint images increases, the performance of recognition can be improved while maintaining the processing time and the size of memory storage for tile super-template almost constant.

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.1 s.24
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• pp.24-32
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• 2006

This paper is to analyze the effectiveness of multistatic sonar network based on detection performance. The multistatic sonar network is a distributed detection system that places a source and multi-receivers apart. So it needs a detection technique that relates to decision rule and optimization of sonar system to improve the detection performance. For this we propose a data fusion procedure using Bayesian decision and optimal sensor arrangement by optimizing a bistatic sonar. Also, to analyze the detection performance effectively, we propose the environmental model that simulates a propagation loss and target strength suitable for multistatic sonar networks in real surroundings. The effectiveness analysis on the multistatic sonar network confirms itself as a promising tool for effective allocation of detection resources in multistatic sonar system.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.4
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• pp.386-392
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• 1990

This paper discribes a sonar-based certainty grid, the probabilistic representation of the uncertain and incomplete sensor knowledge, for autonomous mobile robot navigation. We use sonar sensor range data to build a map of the robot's surroundings. This range data provides information about the location of the objects which may exist in front of the sensor. From this information, we can compute the probability of being occupied and that of being empty for each cell. In this paper, a new method using Bayesian formula is introduced, which enables us to overcome some difficulties of the Ad-Hoc formula that has been the only way of updating the grids. This new formula can be applied to other kinds of sensors as well as sonar sensor. The validity of this formula in the real world is verified through simulation and experiment. This paper also shows that a wide angle sensor such as sonar sensor can be used effectively to identify the empty area, and the simultaneous use of multiple sensors and fusion in a certainty grid can improve the quality of the map.