• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.1 s.307
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• pp.31-38
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• 2006

In this paper, we consider one-step-ahead control of waveform parameters (pulse amplitudes and lengths, and FM sweep rate) as well as detection thresholds for optimal range and range-rate tracking in clutter. The optimal control of the combined parameter set minimizes a tracking performance index under a set of parameter constraints. The performance index includes the probability of track loss and a function of estimation error covariances. The track loss probability and the error covariance are predicted using a hybrid conditional average algorithm The effect of the false alarms and clutter interference is taken into account in the prediction. Tracking performance of the one-step-ahead control is presented for several examples and compared with a control strategy heuristically derived from a finite horizon optimization.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.572-578
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• 2018

This paper considers Group Testing as one of combinatorial problems. The group testing first began to inspect soldier's syphilis infection during World War II and have long established an academic basis. Recently, there has been much interest in related areas because of the rediscovery of the value of the group testing. The group testing is the same as finding a few defect samples out of a large number of samples, which is similar to the inverse problem of Compressed Sensing. In this paper, we introduce the definition of the group testing, and specify the classes of the group testing and the bounds on performance of the group testing. In addition, we show a lower bound for the number of tests required to find defective samples using the theoretical theorem which is mainly used for relationship between conditional entropy and the probability of error in the information theory. We see how our result can be different from other related results.

• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.349-359
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• 2017

During the past 25 years, in the context of probabilistic safety assessment, efforts have been directed towards establishment of comprehensive pipe failure event databases as a foundation for exploratory research to better understand how to effectively organize a piping reliability analysis task. The focused pipe failure database development efforts have progressed well with the development of piping reliability analysis frameworks that utilize the full body of service experience data, fracture mechanics analysis insights, expert elicitation results that are rolled into an integrated and risk-informed approach to the estimation of piping reliability parameters with full recognition of the embedded uncertainties. The discussion in this paper builds on a major collection of operating experience data (more than 11,000 pipe failure records) and the associated lessons learned from data analysis and data applications spanning three decades. The piping reliability analysis lessons learned have been obtained from the derivation of pipe leak and rupture frequencies for corrosion resistant piping in a raw water environment, loss-of-coolant-accident frequencies given degradation mitigation, high-energy pipe break analysis, moderate-energy pipe break analysis, and numerous plant-specific applications of a statistical piping reliability model framework. Conclusions are presented regarding the feasibility of determining and incorporating aging effects into probabilistic safety assessment models.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.473-476
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• 2003

This paper presents a systematic risk assessment procedure with uncertainty modeling for general construction projects. Since the approach is able to effectively deal with all the related construction risks in terms of the assumed probability with conditional probability concept that systematically incorporate expert's experiences and subjective judgement, the proposed methods with uncertainty modeling is able to apply to all the construction projects inherent in lots of uncertain risk events. The fuzzy set theory is adopted to enhance risk assessment to effectively handle the vague and dynamic phenomenon of an event Therefore, the fuzzy-based risk assessment is very useful, for those countries, such as Korea, where objective probabilistic data for risk assessment is extremely rare, and thus the utilization of subjective judgmental data based on expert's experiences is inevitable.

• Journal of Korean Society of Transportation
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• v.19 no.5
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• pp.109-117
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• 2001

The median encroachment accident model proposed in this paper is the first step to develop cost-effective criteria about installing facilities preventing traffic accidents by median encroachment. This model consists of expected annual number of median encroachment on roadway and conditional probability to collide with vehicles on opposite lane after encroachment. Expected encroachment number is related to traffic volume and quote from a study of Hutchinson & Kennedy(1966). The probability of vehicle collision is composed of assumed headway distribution of opposite directional vehicles (negative exponential distribution), driving time of encroaching vehicle and Gap & Gap acceptance model. By using expected accident number yielded from the presented model, it will be able to calculate the benefit of reduced accident and to analyze the cost of installing facilities. Therefore this will help develop cost-effective criteria of what, to install in the median.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.3
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• pp.303-309
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• 2004

In this paper, an adaptive Bayesian approach to image segmentation was developed for boundary detection. Both image intensities and texture information were used for obtaining better quality of the image segmentation by using the C programming language. Fuzzy c-mean clustering was applied fer the conditional probability density function, and Gibbs random field model was used for the prior probability density function. To simply test the algorithm, a synthetic image (256$\times$256) with a set of low gray values (50, 100, 150 and 200) was created and normalized between 0 and 1 n double precision. Results have been presented that demonstrate the effectiveness of the algorithm in segmenting the synthetic image, resulting in more than 99% accuracy when noise characteristics are correctly modeled. The algorithm was applied to the Antarctic mosaic that was generated using 1963 Declassified Intelligence Satellite Photographs. The accuracy of the resulting vector map was estimated about 300-m.

• School Mathematics
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• v.19 no.3
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• pp.461-480
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• 2017

The aim of this study was to analyze characteristics of teachers' knowledge about correlation with data presented in $2{\times}2$ tables. In order to achieve the aim, this study conducted didactical analysis about two-way tables through examining previous researches and developed a questionnaire with reference to the results of the analysis. The questionnaire was given to 53 middle and high school teachers and qualitative methods were used to analyze the data obtained from the written responses by the participants. This study also elaborated the framework descriptors for interpreting the teachers' responses in the light of the didactical analysis and the data was elucidated in terms of this framework. The specific features of teachers' knowledge about correlation with data presented in $2{\times}2$ tables were categorized into three types as a result. This study raised several implications for teachers' professional development for effective mathematics instruction about correlation and related concepts dealt with in probability and statistics.

• Journal of the Korean Geotechnical Society
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• v.37 no.10
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• pp.41-54
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• 2021

In this study, probabilistic steady seepage behavior of soil foundation beneath water retaining structures according to the location of cutoffs was studied. A Monte Carlo Simulation based on the random finite element method that considers the uncertainty and spatial variability of soil permeability was performed to evaluate the probabilistic seepage behavior. Fragility curves were developed by calculating the failure probability conditional on the occurrence of a given water level from the probability distribution obtained from Monte Carlo simulations. The fragility curve was prepared for the flow quantities such as flow rate through foundation soil, uplift force on the base of structure, and exit gradient in downstream to examine the reliability of the water retaining structure and the foundation soil. From the fragility curves, the effect of the location of cutoff wall on the reliability of water retaining structure and foundation soil according to the rise in water level was studied.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.34-39
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• 2019

Millimeter-wave (mmWave) and Non-orthogonal multiple access (NOMA) are expected to be the major techniques that lead to the next generation wireless communication. NOMA provides a high spectrum efficiency by sharing of spatial resources among users in the same frequency band. Meanwhile, millimeter-wave gives a huge underutilized bandwidth at extremely high frequency band (EHF) which covers 30GHz to 300GHz. These techniques have been proven in several recent literatures to achieve high data rates. The combination of NOMA and millimeter-wave techniques further improves average sum capacities, as well as reduces the interference compared to conventional wireless communication systems. In this paper, we focus on hybrid NOMA system working in millimeter-wave frequency. We propose a clustering algorithm used for a hybrid NOMA scheme to optimize the usage of wireless resources. The proposed clustering algorithm adds several conditions in grouping users and defining clusters to increase the probability of the successful superposition decoding process. The performance of the proposed clustering algorithm is investigated in hybrid NOMA system and compared with the conventional orthogonal multiple access (OMA) scheme.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.489-503
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• 2000

Data mining means data analysis and model selection using various types of data in order to explore useful information and knowledge for making decisions. Examples of data mining include scoring for credit analysis of a new customer and scoring for churn management, where the customers with high scores are given special attention. In this paper, scoring is interpreted as a modeling process of the conditional probability and polyclass scoring method is described. German credit data, a PC communication company data and a mobile communication company data are used to compare the performance of polyclass scoring method with that of the scoring method based on a tree model.