• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.367-376
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• 2015

Today, big data has become a hot keyword in that big data may be defined as collection of data sets so huge and complex that it becomes difficult to process by traditional methods. Clustering method is to identify the information in a big database by assigning a set of objects into the clusters so that the objects in the same cluster are more similar to each other clusters. The similarity measures being used in the cluster analysis may be classified into various types depending on the nature of the data. In this paper, we computed upper and lower limits for probability interestingness measure based similarity measures without marginal probability such as Yule I and II, Michael, Digby, Baulieu, and Dispersion measure. And we compared these measures by real data and simulated experiment. By Warrens (2008), Coefficients with the same quantities in the numerator and denominator, that are bounded, and are close to each other in the ordering, are likely to be more similar. Thus, results on bounds provide means of classifying various measures. Also, knowing which coefficients are similar provides insight into the stability of a given algorithm.

• The Journal of Fisheries Business Administration
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• v.47 no.3
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• pp.71-92
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• 2016

This study examines the effects of consumer beliefs for food certifications on the behavioral intentions and the behavioral intention biases to purchase the certified seafoods by a subjective probability model which is on the basis of the mathematical probability model and the covariance model. The food certifications used on this study are 'Organic foods', 'Traceability system of food products' and. 'HACCP'. The representative foods of fishery products on this study is seasoned laver. The current study showed the following results. First, consumers have more than two different beliefs each for all certifications which are the subjects of this study. The beliefs of the certifications have an impact on the consumers when they consider to buy the certified seafood products. Second, consumers try to persuade by themselves to ensure that their particular belief about the certification could lead to a purchase the seafood products. Consumer beliefs of the "environmentally friendly production" on the organic foods certification is an important factor as much as the "guarantee of food safety" belief making a positive purchasing behavior intentions(PBI) bias for the organic seafood products. Consumers also have a positive PBI bias for certified seafood products in all certifications as long as a certification is considered to "guarantee the transparency of the food distribution process" as its belief. 'Traceability system' was the only one which didn't generate a positive PBI bias from the belief of "guarantee of food safety" out of three certifications.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.181-190
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• 2017

Recently, safety accidents of daily necessities such as humidifier disinfectant, mobile phones, and infant diapers, have occurred frequently. To protect consumers from these accidents, product safety management is required, and a product risk assessment tool is needed to evaluate the degree of safety of the product. In this paper, we have constructed RAS, which is a system that can evaluate product risk based on injury information of product accident in Korea Consumer Agency. RAS consists of an injury information analysis system for analyzing accident-related information and a risk assessment system for assessing risk using information derived from the system. The Bayesian network - based probabilistic method is applied to reflect the causal relationships that affect product risk in the risk assessment process. We used RAS to evaluate 33 children's products and compared them with the results of EU RAPEX RAG. Subsequent tasks include reducing the subjectivity of the input of the accident impact scale, and linking the above two systems.

• Journal of Wetlands Research
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• v.11 no.1
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• pp.49-64
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• 2009

Rainfall-runoff models are used for efficient management, distribution, planning, and design of water resources in accordance with the process of hydrologic cycle. The models simplify the transition of rainfall to runoff as rainfall through different processes including evaporation, transpiration, interception, and infiltration. As the models simplify complex physical processes, gaps between the models and actual rainfall events exist. For more accurate simulation, appropriate models that suit analysis goals are selected and reliable long-term hydrological data are collected. However, uncertainty is inherent in models. It is therefore necessary to evaluate reliability of simulation results from models. A number of studies have evaluated uncertainty ingrained in rainfall-runoff models. In this paper, Meta-Gaussian method proposed by Montanari and Brath(2004) was used to assess uncertainty of simulation outputs from rainfall-runoff models. The model, which estimates upper and lower bounds of the confidence interval from probabilistic distribution of a model's error, can quantify global uncertainty of hydrological models. In this paper, Meta-Gaussian method was applied to analyze uncertainty of simulated runoff outputs from $Vflo^{TM}$, a physically-based distribution model and HEC-HMS model, a conceptual lumped model.

• Journal of the Korean Society for Railway
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• v.11 no.4
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• pp.404-409
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• 2008

This paper consists largely of two parts: the first part introduces the revised railway human reliability analysis (R-HRA) method which is to be used under the railway risk assessment framework, and the second part presents the features of a computer software which was developed for aiding the R-HRA process. The revised R-HRA method supplements the original R-HRA method by providing a specific task analysis guideline and a classification of performance shaping factors (PSFs) to support a consistent analysis between analysts. The R-HRA software aids the analysts in gathering information for HRA, qualitative error prediction including identification of external error modes and internal error modes, quantification of human error probability, and reporting the overall analysis results. The revised R-HRA method and software are expected to support the analysts in an effective and efficient way in analysing human error potential in railway event or accident scenarios.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9A
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• pp.891-899
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• 2008

The cdma2000 1xEV-DO system controls the data rates of mobile terminals based on a binary overload indicator from the base station and a simple probabilistic model. However, this traffic control scheme has difficulty in controlling the reverse-link traffic load effectively and in guaranteeing a stable operation of the reverse link because each mobile terminal determines the next data rate autonomously. This paper proposes a new trafRc control scheme to improve the system stability, and analyzes the proposed scheme by modeling it as a discrete-time Markov process. The numerical results show that the maximum data rate of the proposed scheme is much higher than that of the conventional one. Moreover, the proposed scheme does not modify the standard physical channel structure, so it is compatible to the existing 1xEV-DO system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.411-413
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• 2010

The wireless AP positioning system is under active progress regarding research and commercialization due to its merit of being able to overcome the representing demerits of existing GPS positioning, which are signal distortion and poor signal reception. This system's feature is to collect AP information distributed throughout the real world, store it on database, and execute positioning by comparing with searched AP information. The positioning process uses collected data, whereas comparison of database data uses the fingerprinting method. The fingerprinting method is a probabilistic modeling method that acquires as much of the data collected from one location upon database composition, to store the value's average value and use it in positioning. Yet, using the average value may contain the probability of errors. Such errors are fatal weaknesses for services based on the background of accurate positioning. This paper deals with the characteristics and problems of the previously used wireless AP positioning system, and proposes measures of using AP information for outdoor positioning in order to solve the aforementioned problems.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.612-628
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• 2020

This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes where anomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects' behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial context of local behavior and the temporal context of global behavior in two different stages. In the first stage of topic modeling, unlike the existing approaches using either optical flows or complete trajectories, spatio-temporal correlations between the trajectory fragments in video clips were modeled by the latent Dirichlet allocation (LDA) topic model based on Markov random fields to obtain the spatial context of local behavior in each video clip. The local behavior topic categories were then obtained by exploiting the spectral clustering algorithm. Based on the construction of a dictionary through the process of local behavior topic clustering, the second phase of the LDA topic model learns the correlations of global behaviors and temporal context. In particular, an abnormal behavior recognition method was developed based on the learned spatio-temporal context of behaviors. The specific identification method adopts a top-down strategy and consists of two stages: anomaly recognition of video clip and anomalous behavior recognition within each video clip. Evaluation was performed using the validity of spatio-temporal context learning for local behavior topics and abnormal behavior recognition. Furthermore, the performance of the proposed approach in abnormal behavior recognition improved effectively and significantly in complex surveillance scenes.

• Structural Engineering and Mechanics
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• v.80 no.6
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• pp.645-655
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• 2021

Performance-based reliability analysis is a practical approach to investigate the seismic performance and stochastic nonlinear response of structures considering a random process. This is significant due to the uncertainties involved in every aspect of the analysis. Therefore, the present study aims to evaluate the performance-based reliability within a stochastic finite element (FE) framework for reinforced concrete (RC) shear walls that are considered as one of the most essential elements of structures. To accomplish this purpose, deterministic FE analyses are conducted for both squat and slender shear walls to validate numerical models through experimental results. The presented numerical analysis is performed by using the ABAQUS FE program. Afterwards, a random-effects investigation is carried out to consider the influence of different random variables on the lateral load-top displacement behavior of RC members. Using these results and through utilizing the Monte-Carlo simulation method, stochastic nonlinear analyses are also performed to generate random FE models based on input parameters and their probabilistic distributions. In order to evaluate the reliability of RC walls, failure probabilities and corresponding reliability indices are calculated at life safety and collapse prevention levels of performance as suggested by FEMA 356. Moreover, based on reliability indices, capacity reduction factors are determined subjected to shear for all specimens that are designed according to the ACI 318 Building Code. Obtained results show that the lateral load and the compressive strength of concrete have the highest effects on load-displacement responses compared to those of other random variables. It is also found that the probability of shear failure for the squat wall is slightly lower than that for slender walls. This implies that 𝛽 values are higher in a non-ductile mode of failure. Besides, the reliability of both squat and slender shear walls does not change significantly in the case of varying capacity reduction factors.

• Earthquakes and Structures
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• v.24 no.4
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• pp.303-316
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• 2023

Rapid post-earthquake damage estimation of subway stations is particularly necessary to improve short-term crisis management and safety measures of urban subway systems after a destructive earthquake. The conventional Performance-Based Earthquake Engineering (PBEE) framework with constant earthquake occurrence rate is invalid to estimate the aftershock risk because of the time-varying rate of aftershocks and the uncertainty of mainshock-damaged state before the occurrence of aftershocks. This study presents a time-varying probabilistic seismic risk assessment framework for underground structures considering mainshock and aftershock hazards. A discrete non-omogeneous Markov process is adopted to quantify the time-varying nature of aftershock hazard and the uncertainties of structural damage states following mainshock. The time-varying seismic risk of a typical rectangular frame subway station is assessed under mainshock-only (MS) hazard and mainshock-aftershock (MSAS) hazard. The results show that the probabilities of exceeding same limit states over the service life under MSAS hazard are larger than the values under MS hazard. For the same probability of exceedance, the higher response demands are found when aftershocks are considered. As the severity of damage state for the station structure increases, the difference of the probability of exceedance increases when aftershocks are considered. PSDR=1.0% is used as the collapse prevention performance criteria for the subway station is reasonable for both the MS hazard and MSAS hazard. However, if the effect of aftershock hazard is neglected, it can significantly underestimate the response demands and the uncertainties of potential damage states for the subway station over the service life.