• Korean Journal of Cognitive Science
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• v.7 no.4
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• pp.163-178
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• 1996

• Journal of Digital Convergence
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• v.12 no.7
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• pp.273-278
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• 2014

In vocabulary recognition using HMM(Hidden Markov Model) by model for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate. Improve them with a HMM model is proposed for the optimization of the Bayesian methods. In this paper is posterior distribution and prior distribution in recognition Gaussian mixtures model provides a model to optimize of the Bayesian methods vocabulary recognition. The result of applying the proposed method, the recognition rate of 97.9% in vocabulary recognition, respectively.

• Journal of KIISE:Software and Applications
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• v.30 no.9
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• pp.867-875
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• 2003

This paper concerns continuous density HMM topology optimization. There have been several researches for HMM topology optimization. BIC (Bayesian Information Criterion) is one of the well known optimization criteria, which assumes statistically well behaved homogeneous model parameters. HMMs, however, are composed of several different kind of parameters to accommodate complex topology, thus BIC's assumption does not hold true for HMMs. Even though BIC reduced the total number of parameters of HMMs, it could not improve the recognition rates. In this paper, we proposed two new model selection criteria, HBIC (HMM-oriented BIC) and BIC_Anti. The former is proposed to improve BIC by estimating model priors separately. The latter is to combine BIC and anti-likelihood to accelerate discrimination power of HMMs. We performed some comparative research on couple of model selection criteria for online handwriting data recognition. We got better recognition results with fewer number of parameters.

• Journal of Navigation and Port Research
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• v.34 no.6
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• pp.405-415
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• 2010

To assess the possible collision risk between Mokpo Harbour Bridge, which is under construction, and passing vessels, we proposed Real-Time Bridge-Vessel Collision Model (RT-BVCM) in this paper. The mathematical model of RT-BVCM consists of the causation probability by the vessel aberrancy due to navigation environments, the geometric probability by the structural feature of a bridge relative to a ship size and, the failure probability by the ship collision track and the stopping distance which is not to come to a stop before hitting the obstacles. Then, the probabilistic mathematical model represented as risk index with the risk level from 1 to 5. The merit of the proposed model to the collision model proposed by AASHTO (American Association of State Highway and Transportation Officials) is that it can provide enough time to take adequate collision avoiding action. Through the simulation tests to the two kinds of test ships, 3,000 GT and 10,000 GT, it is cleary found that the proposed model can be used as a collision evaluation model to the passing vessel and Mokpo Harbour Bridge.

• The Journal of Engineering Geology
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• v.21 no.3
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• pp.247-257
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• 2011

In this study the infinite slope model, one of the physical landslide models has been suggested to evaluate the susceptibility of the landslide. However, applying the infinite slope model in regional study area can be difficult or impossible because of the difficulties in obtaining and processing of large spatial data sets. With limited site investigation data, uncertainties were inevitably involved with. Therefore, the probabilistic analysis method such as Monte Carlo simulation and the GIS based infinite slope stability model have been used to evaluate the probability of failure. The proposed approach has been applied to practical example. The study area in Boeun area been selected since the area has been experienced tremendous amount of landslide occurrence. The geometric characteristics of the slope and the mechanical properties of soils like to friction angle and cohesion were obtained. In addition, coefficient of variation (COV) values in the uncertain parameters were varied from 10% to 30% in order to evaluate the effect of the uncertainty. The analysis results showed that the probabilistic analysis method can reduce the effect of uncertainty involved in input parameters.

• Journal of Korean Society of Steel Construction
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• v.15 no.5 s.66
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• pp.475-487
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• 2003

This study developed an analysis model of estimating fatigue damage using the linear elastic fracture mechanics method. Stress history occurring to an element when a truck passed over a bridge was defined as block loading and crack closure theory explaining load interaction effect was applied. Stress range frequency analysis considering dead load stress and crack opening was done. Probability of stress range frequency distribution was applied and the probability distribution parameters were estimated. The Monte Carlo simulation of generating the probability various of distribution was performed. The probability distribution of failure block numbers was obtained. With this the fatigue reliability of an element not occurring in failure could be calculated. The failure block number divided by average daily truck traffic remains the life of a day. Fatigue reliability analysis model was carried out for the welding member of cross beam flange and vertical stiffener of steel box bridge using the proposed model. Consequently, a 3.8% difference was observed between the remaining life in the peak analysis method and in the proposed analysis model. The proposed analysis model considered crack closure phase and crack retard.

• Journal of the Korea Convergence Society
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• v.11 no.4
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• pp.33-39
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• 2020

Interest in 5G communication security has been growing recently amid growing expectations for 5G technology with faster speed and stability than LTE. However, 5G has so far included disparate areas, so it has not yet fully supported the issues of security. This paper proposes a blockchain-based IoT management model in order to efficiently provide the authentication of users using IoT in 5G In order to efficiently fuse the authentication of IoT users with probabilistic theory and physical structure, the proposed model uses two random keys in reverse direction at different layers so that two-way authentication is achieved by the managers of layers and layers. The proposed model applied blockchain between grouped IoT devices by assigning weights to layer information of IoT information after certification of IoT users in 5G environment is stratified on a probabilistic basis. In particular, the proposed model has better functions than the existing blockchain because it divides the IoT network into layered, multi-layered networks.

• Journal of Food Hygiene and Safety
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• v.18 no.4
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• pp.237-242
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• 2003

Sequences of industrial microbial counts of foods shows irregular fluctuating patterns as adeinition of fluctuating microbial counts(FMC). Recently, it beame clear that the FMC was considered as having a lognormal distribution as a first order approximation. Application of lognormal distribution to the industrial microbial counts could produce useful information in practice. This study is intended to verift the application method of lognormal idstribution in FMC. The one year's records for microbial counts of frozen dumplings from two companies were obtained, and the statistical analysis was carried out to estimate the frequencies of future events where counts exceeding selected levels and to compare the sanitation level of the two companies. The results showed that this spplication method enable translation of irregular recourds of microbial counts into an useful information such as te actual probalities of outburst of a given level and the quantitative predictions of potential hazards in the processing.

• Journal of KIISE:Software and Applications
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• v.30 no.1_2
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• pp.81-92
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• 2003

Generalized LR parsing is one of the enhanced LR parsing methods so that it overcome the limit of one-way linear stack of the traditional LR parser using graph-structured stack, and it has been playing an important role of a firm starting point to generate other variations for NL parsing equipped with various mechanisms. In this paper, we propose a conditional Action Model that can solve the problems of conventional probabilistic GLR methods. Previous probabilistic GLR parsers have used relatively limited contextual information for disambiguation due to the high complexity of internal GLR stack. Our proposed model uses Surface Phrasal Types representing the structural characteristics of the parse for its additional contextual information, so that more specified structural preferences can be reflected into the parser. Experimental results show that our GLR parser with the proposed Conditional Action Model outperforms the previous methods by about 6-7% without any lexical information, and our model can utilize the rich stack information for syntactic disambiguation of probabilistic LR parser.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.19-26
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• 2005

Image interpolation in the wavelet domain usually takes advantage of the probabilistic models for the intrascale statistics and the interscale dependency. In this paper, we adopt the linear model for the absolute values of wavelet coefficients of interpolated image across scale to estimate the variances of extrapolated bands. The proposed algorithm uses randomly generated wavelet coefficients based on the estimated parameters for probabilistic model. Random number generation according to the estimated probabilistic model may induce the 'salt and pepper' noise in subbands. We reduce the noise power by Wiener filtering. We observe that the proposed method generates the histogram of the subband coefficients similar to the that of original image. Experimental results show that our method outperforms the previous wavelet-domain interpolation method as well as the conventional bicubic method.