• Journal of Information Processing Systems
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• v.16 no.1
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• pp.145-154
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• 2020

Maximum likelihood (ML) is the best estimator asymptotically as the number of training samples approaches infinity. This paper deduces an adaptive algorithm for blind signal processing problem based on gradient optimization criterion. A parametric density model is introduced through a parameterized generalized distribution family in ML framework. After specifying a limited number of parameters, the density of specific original signal can be approximated automatically by the constructed density function. Consequently, signal separation can be conducted without any prior information about the probability density of the desired original signal. Simulations on classical biomedical signals confirm the performance of the deduced technique.

• Journal of the Korean Statistical Society
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• v.34 no.4
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• pp.297-309
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• 2005

We consider 'efficiency versus privacy-protection' problem concerned with several well-known randomized response (RR) devices to estimate pro­portion of people bearing a stigmatizing characteristic in a community. The literature of RR on respondent's privacy protection discusses only about response specific jeopardy measures. We propose a measure of jeopardy that is independent of the RR offered by the interviewee and recommend it for using as a technical characteristic of the RR device. For ensuring better cooperation from the interviewees this new measure that depends only on the design parameters of the RR devices may be disclosed to the respondents before producing the RR by implementing the randomization device.

• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.473-495
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• 2019

In this paper, a new extension of Lindley distribution has been introduced. Certain characterizations based on truncated moments, hazard and reverse hazard function, conditional expectation of the proposed distribution are presented. Besides, these characterizations, other statistical/mathematical properties of the proposed model are also discussed. The estimation of the parameters is performed through different classical methods of estimation. Bayes estimation is computed under gamma informative prior under the squared error loss function. The performances of all estimation methods are studied via Monte Carlo simulations in mean square error sense. The potential of the proposed model is analyzed through two data sets. A modified goodness-of-fit test using the Nikulin-Rao-Robson statistic test is investigated via two examples and is observed that the new extension might be used as an alternative lifetime model.

• East Asian mathematical journal
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• v.38 no.4
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• pp.517-532
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• 2022

This study looked at how content reviews with mathematical games in class would influence the mathematical disposition of middle school students. In doing so, three games adapted from prior research were used as a supplementary instruction after school hours over three months. The mathematical topics of the games involved concepts of probability and trigonometry at the middle school level. The results of the pre- and post-survey on mathematical disposition indicate that incorporating mathematical games appeared to have some positive impacts on whether students might be more eager to learn mathematics and actually put more effort in learning materials.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.2
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• pp.94-106
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• 2010

Understanding of the amount of energy resources and site selection are required prior to develop Ocean Thermal Energy (OTE). It is necessary to calculate the appearance probability of difference of seawater temperature(${\Delta}T$) between sea surface layer and underwater layers. This research mainly aimed to calculate the appearance probability of ${\Delta}T$ using frequency analysis(FA) and harmonic analysis(HA), and compare the advantages and weaknesses of those methods which has used in the South Sea of Korea. Spatial scale for comparison of two methods was divided into local and global scales related to the estimation of energy resources amount and site selection. In global scale, the Probability Differences(PD) of calculated ${\Delta}T$ from using both methods were created as spatial distribution maps, and compared areas of PD. In local scale, both methods were compared with not only the results of PD at the region of highest probability but also bimonthly probabilities in the regions of highest and lowest PD. Basically, the strong relationship(pearson r=0.96, ${\alpha}$=0.05) between probabilities of two methods showed the usefulness of both methods. In global scale, the area of PD more than 10% was less than 5% of the whole area, which means both methods can be applied to estimate the amount of OTE resources. However, in practice, HA method was considered as a more pragmatic method due to its capability of calculating under various ${\Delta}T$ conditions. In local scale, there was no significant difference between the high probability areas by both methods, showing difference under 5%. However, while FA could detect the whole range of probability, HA had a disadvantage of inability of detecting probability less than 10%. Therefore it was analyzed that the HA is more suitable to estimate the amount of energy resources, and FA is more suitable to select the site for OTE development.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.5
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• pp.70-77
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• 2009

In an RFID network consisting of a single reader and many tags, a framed and slotted ALOHA, which provides a number of slots for the tags to respond, was introduced for arbitrating a collision among tags' responses. In a framed and slotted ALOHA, the number of slots in each frame should be optimized to attain the maximal efficiency in tag cognizance. While such an optimization necessitates the knowledge about the number of tags, the reader hardly knows it. In this paper, we propose a tag cognizance scheme based on framed and slotted ALOHA, which is characterized by directly taking a Bayes action on the number of slots without estimating the number of tags separately. Specifically, a Bayes action is yielded by solving a decision problem which incorporates the prior distribution the number of tags, the observation on the number of slots in which no tag responds and the loss function reflecting the cognizance rate. Also, a Bayes action in each frame is supported by an evolution of prior distribution for the number of tags. From the simulation results, we observe that the pair of evolving prior distribution and Bayes action forms a robust scheme which attains a certain level of cognizance rate in spite of a high discrepancy between the Due and initially believed numbers of tags. Also, the proposed scheme is confirmed to be able to achieve higher cognizance completion probability than a scheme using classical estimate of the number of tags separately.

• 한국노년학
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• v.31 no.3
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• pp.527-538
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• 2011

The purpose of this investigation is to analyze the effect of employment and quality of employment of senior job training. The questionnaire administered to 576 senior job trainees(over 50 years old) and 28 training institute before and after job training to survey job training characteristics, training institute characteristics and trainee characteristics. The results were as follows : First, independence test(X2) revealed that occupational category, period of training, type of institution, training history, location, and trainee's education level had significant difference on employment. Second, The probability of employment was higher in new and well-equipped public institution than private or old public institution. Third, compared with the prior wage, the wage after training decreased. This result suggested that the unemployed senior can hardly be reemployed in prior level job. The result of analysis on the cases of increased wage after training revealed that the trainees who was women, had a little dependent family, a shorter unemployed period, and a higher prior wage showed higher wage than prior wage after training.

• The Journal of Bigdata
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• v.8 no.1
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• pp.23-34
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• 2023

Nowadays, the Designated Driver Services employ dynamic pricing, which adapts in real-time based on nearby driver availability, service user volume, and current weather conditions during the user's request. The uncertain volatility is the main cause of price increases, leading to customer attrition and service refusal from driver. To make a good Designated Driver Services, development of a demand forecasting model is required. In this study, we propose developing a demand forecasting model using data from the Designated Driver Service by considering normal and peak periods, such as rush hour and rush day, as prior knowledge to enhance the model performance. We propose a new methodology called Time-Series with Conditional Probability(TSCP), which combines conditional probability and time-series models to enhance performance. Extensive experiments have been conducted with real Designated Driver Service data, and the result demonstrated that our method outperforms the existing time-series models such as SARIMA, Prophet. Therefore, our study can be considered for decision-making to facilitate proactive response in Designated Driver Services.

• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.80-87
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• 2010

This study was conducted to develop a reservoir modelling workflow to reproduce the heterogeneous distribution of effective permeability that impacts on the performance of SAGD (Steam Assisted Gravity Drainage), the in-situ bitumen recovery technique in the Athabasca Oil Sands. Lithologic facies distribution is the main cause of the heterogeneity in bitumen reservoirs in the study area. The target formation consists of sand with mudstone facies in a fluvial-to-estuary channel system, where the mudstone interrupts fluid flow and reduces effective permeability. In this study, the lithologic facies is classified into three classes having different characteristics of effective permeability, depending on the shapes of mudstones. The reservoir modelling workflow of this study consists of two main modules; facies modelling and permeability modelling. The facies modelling provides an identification of the three lithologic facies, using a stochastic approach, which mainly control the effective permeability. The permeability modelling populates mudstone volume fraction first, then transforms it into effective permeability. A series of flow simulations applied to mini-models of the lithologic facies obtains the transformation functions of the mudstone volume fraction into the effective permeability. Seismic data contribute to the facies modelling via providing prior probability of facies, which is incorporated in the facies models by geostatistical techniques. In particular, this study employs a probabilistic neural network utilising multiple seismic attributes in facies prediction that improves the prior probability of facies. The result of using the improved prior probability in facies modelling is compared to the conventional method using a single seismic attribute to demonstrate the improvement in the facies discrimination. Using P-wave velocity in combination with density in the multiple seismic attributes is the essence of the improved facies discrimination. This paper also discusses sand matrix porosity that makes P-wave velocity differ between the different facies in the study area, where the sand matrix porosity is uniquely evaluated using log-derived porosity, P-wave velocity and photographically-predicted mudstone volume.

• Geotechnical Engineering
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• v.11 no.1
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• pp.9-22
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• 1995

The purpose of this study is to propose safety factors of pile bearing capacity based on the reliability analysis. Each prediction method involves various degrees of uncertainties. To account for these uncertainties in a systematic way, the ratios of the measured bearing capacity from pile load tests to the predicted bearing capacity are represented in the form of a probability density function. The safety factor for each design method is obtained so that the probability of pile foundation failure is less than 10-3. The Bayesian theorem is applied in a way that the distribution using static formulae is assumed to be the A-prior and the distribution using dynamic formulae or wave equation based methods is assumed to be the likelihood, and these two are combined to obtain the posterior which has the reduced uncertainty. The results of this study show that static formulae of the pile bearing capacity using the 5.p.7. N-value as well as dynamic formulae are highly unreliable and have to have the safety factor more than 7.4 : the wave equation analysis using PDA(Pile Driving Analyzer) system the most reliable with the safety factor close to 2.7. The safety factor could be reduced certain amount by adoption the Bayes methodology in pile design.