• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.29-33
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• 1998

In this paper, we propose a hierarchical segmentation method for tracking a semantic video object using a watershed algorithm based on morphological filtering. In the proposed method, each hierarchy consists of three steps: First, markers are extracted on the simplified current frame. Second, region growing by a modified watershed algorithm is performed for segmentation. Finally, the segmented regions are classified into 3 categories, i.e., inside, outside, and uncertain regions according to region probability values, which are acquired by the probability map calculated from a estimated motion field. Then, for the remaining uncertain regions, the above three steps are repeated at lower hierarchies with less simplified frames until every region is decided to a certain region. The proposed algorithm provides prospective results in video sequences such as Miss America, Clair, and Akiyo.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.6
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• pp.17-24
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• 1994

This paper is a study on Korean word recognition and suggest the method that stabilizes the state-transition in the HMM by applying the `inertia' to the feature vector sequences. In order to reduce the quantized distortion considering probability distribution of input vectors, we used SOFM, an unsupervised learning method, as a vector quantizer, By applying inertia to the feature vector sequences, the overlapping of probability distributions for the response path of each word on the self organizing feature map can be reduced and the state-transition in the Hmm can be Stabilized. In order to evaluate the performance of the method, we carried out experiments for 50 DDD area names. The results showed that applying inertia to the feature vector sequence improved the recognition rate by 7.4% and can make more HMMs available without reducing the recognition rate for the SOFM having the fixed number of neuron.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.6
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• pp.726-732
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• 2013

It is required for UGV(Unmanned Ground Vehicle) to have a LPP(Local Path Plan) component which generate a local path via the center of road by analyzing binary map to travel autonomously unpaved road in rough environment. In this paper, we present the method of boundary estimation for unpaved road and a local path planning method based on RANGER algorithm using the estimated boundary. In specially, the paper presents an approach to estimate road boundary and the selection method of candidate path to minimize the problem of zigzag driving based on Bayesian probability reasoning. Field test is conducted with scenarios in rough environment in which bush, tree and unpaved road are included and the performance of proposed method is validated.

• Fire Science and Engineering
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• v.19 no.3 s.59
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• pp.64-69
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• 2005

In order to decrease the area damaged by forest fires and to prevent the occurrence of forest fires, we are making an effort to improve prevention measures for forest fires. The objective of this study is developing the forest fire occurrence probability model by means of forest site characteristics such as soil type, topography, soil texture, slope, and drainage and forest fire sites. Conditional probability analysis and GIS were used in developing the forest fire occurrence probability model that was used in the classification of forest fire occurrence risk regions.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.90.1-90.1
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• 2011

We are developing empirical space weather (geomagnetic storms, solar proton events, and solar flares) forecast models based on solar information. These models have been set up with the concept of probabilistic forecast using historical events. Major findings can be summarized as follows. First, we present a concept of storm probability map depending on CME parameters (speed and location). Second, we suggested a new geoeffective CME parameter, earthward direction parameter, directly observable from coronagraph observations, and demonstrated its importance in terms of the forecast of geomagnetic storms. Third, the importance of solar magnetic field orientation for storm occurrence was examined. Fourth, the relationship among coronal hole-CIR-storm relationship has been investigated, Fifth, the CIR forecast based on coronal hole information is possible but the storm forecast is challenging. Sixth, a new solar proton event (flux, strength, and rise time) forecast method depending on flare parameters (flare strength, duration, and longitude) as well as CME parameter (speed, angular width, and longitude) has been suggested. Seventh, we are examining the rates and probability of solar flares depending on sunspot McIntosh classification and its area change (as a proxy of flux change). Our results show that flux emergence greatly enhances the flare probability, about two times for flare productive sunspot regions.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.10
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• pp.831-841
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• 1991

A combined model of probabilistic and MLP(multi layer perceptron) model is proposed for the pattern classification of EMG( electromyogram) signals. The MLP model has a problem of not guaranteeing the global minima of error and different quality of approximations to Bayesian probabilities. The probabilistic model is, however, closely related to the estimation error of model parameters and the fidelity of assumptions. A proper combination of these will reduce the effects of the problems and be robust to input variations. Proposed model is able to get the MAP(maximum a posteriori probability) in the probabilistic model by estimating a priori probability distribution using the MLP model adaptively. This method minimize the error probability of the probabilistic model as long as the realization of the MLP model is optimal, and this is a good combination of the probabilistic model and the MLP model for the usage of MLP model reliability. Simulation results show the benefit of the proposed model compared to use the Mlp and the probabilistic model seperately and the average calculation time fro classification is about 50ms in the case of combined motion using an IBM PC 25 MHz 386model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.905-908
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• 2008

In this paper, an Adaptive Modulation (AM) system with an efficient turbo-coded Vertical-Bell-lab Layered Space-Time (V-BLAST) technique is proposed. The proposed decoding algorithm adopts iteratively the extrinsic information from a Maximum a Posteriori (MAP) decoder as a priori probability in the two decoding procedures of the V-BLAST scheme of ordering and slicing. In this analysis, each MIMO channel is assumed to be a part of the system of performance improvement.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.27-29
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• 2004

This paper presents a sensor model design based on Monte Carlo Localization method. First, we define the measurement error of each sample using a map matching method by 2-D laser scanners and a pre-constructed grid-map of the environment. Second, samples are assigned probabilities due to matching errors from the gaussian probability density function considered of the sample's convergence. Simulation using real environment data shows good localization results by the designed sensor model.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.430-433
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• 2005

In this study, weight of evidence(WOE) technique based on the bayesian method was applied to estimate the groundwater yield characteristics in the Pocheon area in Kyungki-do. The ground water preservation depends on many hydrogeologic factors that include hydrologic data, landuse data, topographic data, geological map and other natural materials, even with man-made things. All these data can be digitally collected and managed by GIS database. In the applied technique of WOE, The prior probabilities were estimated as the factors that affect the yield on lineament, geology, drainage pattern or river system density, landuse and soil. We calculated the value of the Weight W+, W- of each factor and estimated the contrast value of it. Results by the ground water yield characteristic calculations were presented in the form of posterior probability map to the consideration of in-situ samples. It is concluded that this technique is regarded as one of the effective technique for the feasibility mapping related to detection of groundwater bearing zones and its spatial pattern.

• 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.